There is an ebook version. If you wish to use it for your courses, please feel free to contact fo... more There is an ebook version. If you wish to use it for your courses, please feel free to contact for a possible copy.
There is an ebook version for this book. If you wish to use this book for your courses, please fe... more There is an ebook version for this book. If you wish to use this book for your courses, please feel free to contact me for a possible copy.
Nature-Inspired Computation in Navigation and Routing Problems, 2020
Navigation abilities are crucial for survival in nature, and there are a wide range of sophistica... more Navigation abilities are crucial for survival in nature, and there are a wide range of sophisticated abilities concerning animal navigation and migration. Many applications are related to navigation and routing problems, which are in turn related to optimization problems. This chapter provides an overview of navigation in nature, navigation and routing problems as well as their mathematical formulations. We will then introduce some nature-inspired algorithms for solving optimization problems with discussions about their main characteristics and the ways of solution representations. Citation Detail:
Introduction to Algorithms for Data Mining and Machine Learning (book) introduces the essential ... more Introduction to Algorithms for Data Mining and Machine Learning (book) introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data.
A sample chapter of the Book on
"Bio-inspired Computation and Applications in Image Processing"
... more A sample chapter of the Book on "Bio-inspired Computation and Applications in Image Processing" (Elsevier, 2016).
Firefly algorithm (FA) was developed by Xin-She Yang in 2008, while 1 cuckoo search (CS) was deve... more Firefly algorithm (FA) was developed by Xin-She Yang in 2008, while 1 cuckoo search (CS) was developed by Xin-She Yang and Suash Deb in 2009. Both 2 algorithms have been found to be very efficient in solving global optimization prob-3 lems. This chapter provides an overview of both cuckoo search and firefly algorithm 4
There is an ebook version. If you wish to use it for your courses, please feel free to contact fo... more There is an ebook version. If you wish to use it for your courses, please feel free to contact for a possible copy.
There is an ebook version for this book. If you wish to use this book for your courses, please fe... more There is an ebook version for this book. If you wish to use this book for your courses, please feel free to contact me for a possible copy.
Nature-Inspired Computation in Navigation and Routing Problems, 2020
Navigation abilities are crucial for survival in nature, and there are a wide range of sophistica... more Navigation abilities are crucial for survival in nature, and there are a wide range of sophisticated abilities concerning animal navigation and migration. Many applications are related to navigation and routing problems, which are in turn related to optimization problems. This chapter provides an overview of navigation in nature, navigation and routing problems as well as their mathematical formulations. We will then introduce some nature-inspired algorithms for solving optimization problems with discussions about their main characteristics and the ways of solution representations. Citation Detail:
Introduction to Algorithms for Data Mining and Machine Learning (book) introduces the essential ... more Introduction to Algorithms for Data Mining and Machine Learning (book) introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data.
A sample chapter of the Book on
"Bio-inspired Computation and Applications in Image Processing"
... more A sample chapter of the Book on "Bio-inspired Computation and Applications in Image Processing" (Elsevier, 2016).
Firefly algorithm (FA) was developed by Xin-She Yang in 2008, while 1 cuckoo search (CS) was deve... more Firefly algorithm (FA) was developed by Xin-She Yang in 2008, while 1 cuckoo search (CS) was developed by Xin-She Yang and Suash Deb in 2009. Both 2 algorithms have been found to be very efficient in solving global optimization prob-3 lems. This chapter provides an overview of both cuckoo search and firefly algorithm 4
Many optimization problems in engineering and industrial design applications can be formulated as... more Many optimization problems in engineering and industrial design applications can be formulated as optimization problems with highly nonlinear objectives, subject to multiple complex constraints. Solving such optimization problems requires sophisticated algorithms and optimization techniques. A major trend in recent years is the use of nature-inspired metaheustic algorithms (NIMA). Despite the popularity of nature-inspired metaheuristic algorithms, there are still some challenging issues and open problems to be resolved. Two main issues related to current NIMAs are: there are over 540 algorithms in the literature, and there is no unified framework to understand the search mechanisms of different algorithms. Therefore, this paper attempts to analyse some similarities and differences among different algorithms and then presents a generalized evolutionary metaheuristic (GEM) in an attempt to unify some of the existing algorithms. After a brief discussion of some insights into nature-inspired algorithms and some open problems, we propose a generalized evolutionary metaheuristic algorithm to unify more than 20 different algorithms so as to understand their main steps and search mechanisms. We then test the unified GEM using 15 test benchmarks to validate its performance. Finally, further research topics are briefly discussed.
Almost all optimization algorithms have algorithm-dependent parameters, and the setting of such p... more Almost all optimization algorithms have algorithm-dependent parameters, and the setting of such parameter values can significantly influence the behavior of the algorithm under consideration. Thus, proper parameter tuning should be carried out to ensure that the algorithm used for optimization performs well and is sufficiently robust for solving different types of optimization problems. In this study, the Firefly Algorithm (FA) is used to evaluate the influence of its parameter values on its efficiency. Parameter values are randomly initialized using both the standard Monte Carlo method and the Quasi Monte-Carlo method. The values are then used for tuning the FA. Two benchmark functions and a spring design problem are used to test the robustness of the tuned FA. From the preliminary findings, it can be deduced that both the Monte Carlo method and Quasi-Monte Carlo method produce similar results in terms of optimal fitness values. Numerical experiments using the two different methods on both benchmark functions and the spring design problem showed no major variations in the final fitness values, irrespective of the different sample values selected during the simulations. This insensitivity indicates the robustness of the FA.
Proper heat transfer management is important to key electronic components in microelectronic appl... more Proper heat transfer management is important to key electronic components in microelectronic applications. Pulsating heat pipes (PHP) can be an efficient solution to such heat transfer problems. However, mathematical modelling of a PHP system is still very challenging, due to the complexity and multiphysics nature of the system. In this work, we present a simplified, two-phase heat transfer model, and our analysis shows that it can make good predictions about startup characteristics. Furthermore, by considering parameter estimation as a nonlinear constrained optimization problem, we have used the firefly algorithm to find parameter estimates efficiently. We have also demonstrated that it is possible to obtain good estimates of key parameters using very limited experimental data.
Full bibliographic details must be given when referring to, or quoting from full items including ... more Full bibliographic details must be given when referring to, or quoting from full items including the author's name, the title of the work, publication details where relevant (place, publisher, date), pagination, and for theses or dissertations the awarding institution, the degree type awarded, and the date of the award.
Full bibliographic details must be given when referring to, or quoting from full items including ... more Full bibliographic details must be given when referring to, or quoting from full items including the author's name, the title of the work, publication details where relevant (place, publisher, date), pagination, and for theses or dissertations the awarding institution, the degree type awarded, and the date of the award.
Full bibliographic details must be given when referring to, or quoting from full items including ... more Full bibliographic details must be given when referring to, or quoting from full items including the author's name, the title of the work, publication details where relevant (place, publisher, date), pagination, and for theses or dissertations the awarding institution, the degree type awarded, and the date of the award.
Abstract Modern metaheuristic algorithms are in general suited for global optimization. This pape... more Abstract Modern metaheuristic algorithms are in general suited for global optimization. This paper combines the recently developed eagle strategy algorithm with differential evolution. The new algorithm, denoted as the ES–DE, is implemented by interfacing SAP2000 structural analysis code and MATLAB mathematical software. The performance of the ES–DE is evaluated by solving four benchmark problems where the objective is to minimize the weight of steel frames. The optimized designs obtained by the proposed algorithm are better than those found by the standard differential evolution algorithm and also very competitive with literature. The overall convergence behavior is significantly enhanced by the hybrid optimization strategy.
Communications in Computer and Information Science, 2011
Business optimization is becoming increasingly important because all business activities aim to m... more Business optimization is becoming increasingly important because all business activities aim to maximize the profit and performance of products and services, under limited resources and appropriate constraints. Recent developments in support vector machine and metaheuristics show many advantages of these techniques. In particular, particle swarm optimization is now widely used in solving tough optimization problems. In this paper, we use a combination of a recently developed Accelerated PSO and a nonlinear support vector machine to form a framework for solving business optimization problems. We first apply the proposed APSO-SVM to production optimization, and then use it for income prediction and project scheduling. We also carry out some parametric studies and discuss the advantages of the proposed metaheuristic SVM.
Computer simulations are ubiquitous in contemporary engineering and science. In numerous fields, ... more Computer simulations are ubiquitous in contemporary engineering and science. In numerous fields, including mechanical engineering, civil engineering, electrical engineering, structural and aerospace engineering, automotive industry, oil industry, chemical engineering, ocean science and climate research to name just a few, simulation plays a critical role not only for verification purposes, but, more importantly in the design process itself. The complexity of structures and systems makes it analytically intractable, and it is thus extremely time-consuming and challenging to carry out any realistic design tasks, and in many cases, it is almost impossible to achieve any sensible design solutions under stringent constraints. These challenging tasks can be to optimally adjust the geometry and/or material parameters so that the system meets given performance requirements, or to calibrate the model parameters to make it fit given measurements, or to generate the optimal paths/routes for scheduling and planning tasks. In most cases, the interactions can be highly complex and multifold, and it is not easy or possible to isolate the processes of interest in the simplest, solvable form. For example, in the design of an electronic device, it is not just the isolated device to be designed that needs to be considered but also its-sometimes complex-interactions with the environment that affect the device's performance. On the other hand, using accurate, realistic simulations allows the engineers to avoid costly prototyping and to realize the design closure with numerical models rather than through physical system measurements and prototype rebuilding. Furthermore, accurate simulations make it possible to analyze phenomena that could not be captured using simplistic theoretical models or too expensive or too time-consuming to be investigated through physical measurements. While high-fidelity numerical models can be very accurate, they tend to be computationally expensive. Simulation times of several hours, days, or weeks are not uncommon. In many cases, it may be a highly challenging task to just set up the model that takes into account all main, relevant system components and their interactions. One of the consequences is that a direct use of high-fidelity simulations in the optimization process may be prohibitive. The presence of massive computing resources is not always translated into computational speedup in practice, which is due to a growing demand for simulation
The performance of any algorithm will largely depend on the setting of its algorithmdependent par... more The performance of any algorithm will largely depend on the setting of its algorithmdependent parameters. The optimal setting should allow the algorithm to achieve the best performance for solving a range of optimization problems. However, such parameter-tuning itself is a tough optimization problem. In this paper, we present a framework for self-tuning algorithms so that an algorithm to be tuned can be used to tune the algorithm itself. Using the firefly algorithm as an example, we show that this framework works well. It is also found that different parameters may have different sensitivities, and thus require different degrees of tuning. Parameters with high sensitivities require fine-tuning to achieve optimality.
Proper heat transfer management is important to key electronic components in microelectronic appl... more Proper heat transfer management is important to key electronic components in microelectronic applications. Pulsating heat pipes (PHP) can be an efficient solution to such heat transfer problems. However, mathematical modelling of a PHP system is still very challenging, due to the complexity and multiphysics nature of the system. In this work, we present a simplified, two-phase heat transfer model, and our analysis shows that it can make good predictions about startup characteristics. Furthermore, by considering parameter estimation as a nonlinear constrained optimization problem, we have used the firefly algorithm to find parameter estimates efficiently. We have also demonstrated that it is possible to obtain good estimates of key parameters using very limited experimental data.
International Journal of Bio-Inspired Computation, 2012
Efficiency of an optimization process is largely determined by the search algorithm and its funda... more Efficiency of an optimization process is largely determined by the search algorithm and its fundamental characteristics. In a given optimization, a single type of algorithm is used in most applications. In this paper, we will investigate the Eagle Strategy recently developed for global optimization, which uses a two-stage strategy by combing two different algorithms to improve the overall search efficiency. We will discuss this strategy with differential evolution and then evaluate their performance by solving real-world optimization problems such as pressure vessel and speed reducer design. Results suggest that we can reduce the computing effort by a factor of up to 10 in many applications.
Multiobjective design optimization problems require multiobjective optimization techniques to sol... more Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this paper, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of multobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis are highlighted and discussed.
Optimization techniques play an important role in several scientific and real-world applications,... more Optimization techniques play an important role in several scientific and real-world applications, thus becoming of great interest for the community. As a consequence, a number of open-source libraries are available in the literature, which ends up fostering the research and development of new techniques and applications. In this work, we present a new library for the implementation and fast prototyping of nature-inspired techniques called LibOPT. Currently, the library implements 15 techniques and 112 benchmarking functions, as well as it also supports 11 hypercomplex-based optimization approaches, which makes it one of the first of its kind. We showed how one can easily use and also implement new techniques in LibOPT under the C paradigm. Examples are provided with samples of source-code using benchmarking functions.
MOFPA--Multi-objective flower pollination algorithm. This demo solves a bi-objective ZDT function... more MOFPA--Multi-objective flower pollination algorithm. This demo solves a bi-objective ZDT function of D=30 (dimensions), which can be extended to solve other multi-objective optimization problems. It is relatively straightforward to extend this code to solve other multi-objective functions and optimization problems. You can change the objective functions, dimensionality, various parameters, and simple lower and upper bounds (Lb, Ub).
X.-S. Yang, M. Karamanoglu, X.-S. He, Flower pollination algorithm: A novel approach for multiobjective optimization, Engineering Optimization, vol. 46, no. 9, 1222-1237 (2014).
[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. In addition, for the multi-objective codes, Octave can be very slow for the test problem with 30 dimensions given in the demo codes, so please modify the relevant part of the codes to display results more frequently to show the progress. At the moment, the results are displayed every 100 iterations.]
The multiobjective firefly algorithm (MOFA) is a nature-inspired optimization algorithm. This dem... more The multiobjective firefly algorithm (MOFA) is a nature-inspired optimization algorithm. This demo solves the bi-objective ZDT3 functions with D=30 (dimensions), and the obtained Pareto Front is displayed. It is relatively straightforward to extend this code to solve other multi-objective functions and optimization problems. You can change the objective functions, the dimensionality, and simple lower and upper bounds (Lb, Ub). Some parameter tuning to vary parameters slightly (such as theta, gamma, and number of iterations) may help improve the quality of the solutions.
Yang, Xin-She. “Multiobjective Firefly Algorithm for Continuous Optimization.” Engineering with Computers, vol. 29, no. 2, Springer Science and Business Media LLC, Jan. 2012, pp. 175–84, doi:10.1007/s00366-012-0254-1.
[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. In addition, for the multi-objective codes, Octave can be very slow for the test problem with 30 dimensions given in the demo codes, so please modify the relevant part of the codes to display results more frequently to show the progress. At the moment, the results are displayed every 100 iterations.]
The multiobjective cuckoo search (MOCS) is a nature-inspired optimization algorithm. This demo so... more The multiobjective cuckoo search (MOCS) is a nature-inspired optimization algorithm. This demo solves the bi-objective ZDT3 functions with D=30 (dimensions), and the obtained Pareto Front is displayed. It is relatively straightforward to extend this code to solve other multi-objective functions and optimization problems. You can change the objective functions, dimensionality, various parameters, and simple lower and upper bounds (Lb, Ub).
Yang, Xin-She, and Suash Deb. “Multiobjective Cuckoo Search for Design Optimization.” Computers & Operations Research, vol. 40, no. 6, Elsevier BV, June 2013, pp. 1616–24, doi:10.1016/j.cor.2011.09.026.
[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. In addition, for the multi-objective codes, Octave can be very slow for the test problem with 30 dimensions given in the demo codes, so please modify the relevant part of the codes to display results more frequently to show the progress. At the moment, the results are displayed every 100 iterations.]
The standard flower pollination algorithm (FPA) is inspired by the pollination characteristics of... more The standard flower pollination algorithm (FPA) is inspired by the pollination characteristics of flowering plants. This demo solves the Ackley function of D=10 dimensions. It is straightforward to extend it to solve other functions and optimization problems.
[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. ]
The multiobjective bat algorithm (MOBA) is a nature-inspired optimization algorithm. This demo so... more The multiobjective bat algorithm (MOBA) is a nature-inspired optimization algorithm. This demo solves the bi-objective ZDT3 functions with D=30 (dimensions), and the obtained Pareto Front is displayed. It is relatively straightforward to extend this code to solve other multi-objective functions and optimization problems. You can change the objective functions, the dimensionality, and simple lower and upper bounds (Lb, Ub) as well as certain parameters.
Yang, Xin She. “Bat Algorithm for Multi-Objective Optimisation.” International Journal of Bio-Inspired Computation, vol. 3, no. 5, Inderscience Publishers, 2011, p. 267, doi:10.1504/ijbic.2011.042259.
[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. In addition, for the multi-objective codes, Octave can be very slow for the test problem with 30 dimensions given in the demo codes, so please modify the relevant part of the codes to display results more frequently to show the progress. At the moment, the results are displayed every 100 iterations.]
The standard bat algorithm (BA) is inspired by the echolocation characteristics of microbats. Thi... more The standard bat algorithm (BA) is inspired by the echolocation characteristics of microbats. This demo solves a function of d=10 dimensions. It is straightforward to extend it to solve other functions and optimization problems.
[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. ]
The accelerated particle swarm optimization (APSO) uses only the global best without individual b... more The accelerated particle swarm optimization (APSO) uses only the global best without individual best solutions and reduced randomness. This demo solves a function of D=30 dimensions. It is straightforward to extend it to solve other functions and optimization problems.
[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. ]
The standard firefly algorithm is inspired by the flashing patterns of tropical fireflies. This d... more The standard firefly algorithm is inspired by the flashing patterns of tropical fireflies. This demo solves a function of d=10 dimensions. It is straightforward to extend it to solve other functions and optimization problems.
[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. ]
The standard cuckoo search algorithm is inspired by the evolutionary characteristics of cuckoo-ho... more The standard cuckoo search algorithm is inspired by the evolutionary characteristics of cuckoo-host interactions. This demo solves a function of d=15 dimensions. It is straightforward to extend it to solve other functions and optimization problems.
[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. ]
This presentation introduces the fundamental ideas of nature-inspired optimization algorithms, ba... more This presentation introduces the fundamental ideas of nature-inspired optimization algorithms, based on the book by Xin-She Yang, Nature-Inspired Optimization Algorithms, Elsevier (2014).
These slides also contain the links to the Matlab codes at the Matlabcentral of Mathswork https://uk.mathworks.com/matlabcentral/profile/authors/3659939-xs-yang The numerical simulations using the Matlab codes are also provided as videos at Youtube and the links are automatically connected within the slides.
This presentation explains the fundamental ideas of the standard Flower Pollination Algorithm (FP... more This presentation explains the fundamental ideas of the standard Flower Pollination Algorithm (FPA), which also contains the links to the free Matlab codes at Mathswork file exchanges and the animations of numerical simulations (video at Youtube). An example of multi-objective flower pollination algorithm (MOPFA) is also given with link to the Matlab code.
This presentation explains the fundamental ideas of the standard Cuckoo Search (CS) algorithm, wh... more This presentation explains the fundamental ideas of the standard Cuckoo Search (CS) algorithm, which also contains the links to the free Matlab codes at Mathswork file exchanges and the animations of numerical simulations (video at Youtube). An example of multi-objective cuckoo search (MOCS) is also given with link to the Matlab code.
This presentation explains the fundamental ideas of the bat algorithm (BA), which also contains t... more This presentation explains the fundamental ideas of the bat algorithm (BA), which also contains the links to the free Matlab codes at Mathswork file exchanges and the animations of numerical simulations (video at Youtube).
This presentation introduces the standard firefly algorithm (FA), which also contains the links t... more This presentation introduces the standard firefly algorithm (FA), which also contains the links to the Matlab code (downloadable at Mathswork File Exchange) and the numerical simulations at Youtube.
These slides are the keynote talk by Xin-She Yang at LION2019 Learning and Intelligent Optimizati... more These slides are the keynote talk by Xin-She Yang at LION2019 Learning and Intelligent Optimization Conference (Crete, Greece) .
This is the Tutorial given by Xin-She Yang at MOD2017 (Italy) -- The Third International Conferen... more This is the Tutorial given by Xin-She Yang at MOD2017 (Italy) -- The Third International Conference on Machine Learning, Optimization and Big Data (Sept 14-17, 2017) http://mod2017.taosciences.org/program/
Uploads
Books by Xin-She Yang
http://www.sciencedirect.com/science/book/9780128097304
"Bio-inspired Computation and Applications in Image Processing"
(Elsevier, 2016).
http://www.sciencedirect.com/science/book/9780128097304
"Bio-inspired Computation and Applications in Image Processing"
(Elsevier, 2016).
X.-S. Yang, M. Karamanoglu, X.-S. He, Flower pollination algorithm: A novel approach for multiobjective optimization, Engineering Optimization, vol. 46, no. 9, 1222-1237 (2014).
[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. In addition, for the multi-objective codes, Octave can be very slow for the test problem with 30 dimensions given in the demo codes, so please modify the relevant part of the codes to display results more frequently to show the progress. At the moment, the results are displayed every 100 iterations.]
Yang, Xin-She. “Multiobjective Firefly Algorithm for Continuous Optimization.” Engineering with Computers, vol. 29, no. 2, Springer Science and Business Media LLC, Jan. 2012, pp. 175–84, doi:10.1007/s00366-012-0254-1.
[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. In addition, for the multi-objective codes, Octave can be very slow for the test problem with 30 dimensions given in the demo codes, so please modify the relevant part of the codes to display results more frequently to show the progress. At the moment, the results are displayed every 100 iterations.]
Yang, Xin-She, and Suash Deb. “Multiobjective Cuckoo Search for Design Optimization.” Computers & Operations Research, vol. 40, no. 6, Elsevier BV, June 2013, pp. 1616–24, doi:10.1016/j.cor.2011.09.026.
[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. In addition, for the multi-objective codes, Octave can be very slow for the test problem with 30 dimensions given in the demo codes, so please modify the relevant part of the codes to display results more frequently to show the progress. At the moment, the results are displayed every 100 iterations.]
The details can be found in the book: Xin-She Yang, Nature-Inspired Optimization Algorithms, Elsevier Insights, (2014). https://www.sciencedirect.com/book/9780124167438/nature-inspired-optimization-algorithms
[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. ]
Yang, Xin She. “Bat Algorithm for Multi-Objective Optimisation.” International Journal of Bio-Inspired Computation, vol. 3, no. 5, Inderscience Publishers, 2011, p. 267, doi:10.1504/ijbic.2011.042259.
[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. In addition, for the multi-objective codes, Octave can be very slow for the test problem with 30 dimensions given in the demo codes, so please modify the relevant part of the codes to display results more frequently to show the progress. At the moment, the results are displayed every 100 iterations.]
The details can be found in the book: Xin-She Yang, Nature-Inspired Optimization Algorithms, Elsevier Insights, (2014). https://www.sciencedirect.com/book/9780124167438/nature-inspired-optimization-algorithms
[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. ]
The details can be found in the book: Xin-She Yang, Nature-Inspired Optimization Algorithms, Elsevier Insights, (2014). https://www.sciencedirect.com/book/9780124167438/nature-inspired-optimization-algorithms
[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. ]
The details can be found in the book: Xin-She Yang, Nature-Inspired Optimization Algorithms, Elsevier Insights, (2014). https://www.sciencedirect.com/book/9780124167438/nature-inspired-optimization-algorithms
[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. ]
The details can be found in the book: Xin-She Yang, Nature-Inspired Optimization Algorithms, Elsevier Insights, (2014). https://www.sciencedirect.com/book/9780124167438/nature-inspired-optimization-algorithms
[Notes: Though this demo should work well using either Matlab (preferred) or Octave (free), Matlab can run more smoothly, whereas Octave can be slower. ]
These slides also contain the links to the Matlab codes at the Matlabcentral of Mathswork
https://uk.mathworks.com/matlabcentral/profile/authors/3659939-xs-yang
The numerical simulations using the Matlab codes are also provided as videos at Youtube
and the links are automatically connected within the slides.