Home \ Metaheuristics (page 2)

Metaheuristics

Biogeography-Based Optimization (BBO) in MATLAB

Biogeography-Based Optimization (BBO) is an evolutionary algorithm and metaheuristic, which is inspired by the biogeographic concepts: speciation (the evolution of new species), the migration of species between islands, and the extinction of species. The algorithm is originally proposed by Dan Simon, in 2008, in this paper. This algorithm is based on a mathematical model, describing the migration of species between ...

Read More »

CMA-ES in MATLAB

Evolution Strategy (ES) is the first and oldest evolutionary algorithm, and it is based on the adaptation and evolution. Specially, the main concept used to describe how Evolution Strategy works, is the Evolution of Evolution. In fact, evolution strategy is a family of related algorithms, and because of this, the plural name Evolution Strategies is also widely used, in the ...

Read More »

Differential Evolution (DE) in MATLAB

Differential Evolution (DE) is an evolutionary algorithm, which uses the difference of solution vectors to create new candidate solutions. The key points, in the usage of population differences in proposition of new solutions, are: The distribution of population and its orientation is hidden in the differences of population members. According to the Central Limit Theorem, as the population size increases, ...

Read More »

Simulated Annealing in MATLAB

Simulated Annealing (SA) is a metaheuristic, inspired by annealing process. SA starts with an initial solution at higher temperature, where the changes are accepted with higher probability. So the exploration capability of the algorithm is high and the search space can be explored widely. As the algorithm continues to run, the temperature decreases gradually, like the annealing process, and the ...

Read More »

Harmony Search in MATLAB

Harmony Search (HS) is a global optimization algorithm which inspired by harmony improvisation process of musicians, proposed by Zong Woo Geem in 2001. Every solution in this algorithm is called a Harmony and there is an archive of promising solutions, called Harmony Memory (HM). For more information on Harmony Search, you can read the related article in Wikipedia (here). At ...

Read More »

Teaching-Learning-based Optimization in MATLAB

Teaching-Learning-based Optimization (TLBO) is a metaheuristic, inspired by process of Teaching and Learning, via a simplified mathematical model of knowledge improvements gained by students in a class. This algorithm is proposed by Rao, Savsani and Vakharia in 2011, in this paper. In this post, we are going to share with you, the open-source MATLAB implementation of Teaching-Learning-based Optimization (TLBO) algorithm. ...

Read More »

Shuffled Complex Evolution in MATLAB

Shuffled Complex Evolution (SCE-UA) is a metaheuristic for global optimization, proposed by Duan, Gupta and Sorooshian, in 1992. Because SCE is the abbreviated name of other methods in the science, the UA is added to the abbreviated name of this algorithm, because the creators of this algorithm are members of University of Arizona. In SCE-UA, the population is divided into ...

Read More »

Shuffled Frog Leaping Algorithm in MATLAB

Shuffled Frog Leaping Algorithm (SFLA) is a metaheuristic, or more accurately it is a Memetic Algorithm, which is inspired by frog leaping. SFLA is based on the model used by Shuffled Complex Evolution (SCE-UA), and incorporated the memetic evolution into it. It is applicable to any kind of optimization problems, discrete, continuous or mixed, via modification of operators used in ...

Read More »

ACO for Continuous Domains in MATLAB

Originally, the Ant Algorithms are used to solve discrete and combinatorial optimization problems. Various extensions of Ant Colony Optimization (ACO) are proposed to deal with optimization problems, defined in continuous domains. One of the most useful algorithms of this type, is ACOR, the Ant Colony Optimization for Continuous Domains, proposed by Socha and Dorigo, in 2008 (here). ACOR is an ...

Read More »

Ant Colony Optimization in MATLAB

Ant Colony Optimization (ACO) are a set of probabilistic metaheuristics and an intelligent optimization algorithms, inspired by social behavior of ants. ACO algorithms are also categorized as Swarm Intelligence methods, because of implementation of this paradigm, via simulation of ants behavior in the structure of these algorithms. First ACO algorithm is proposed by Marco Dorigo in his PhD thesis, in ...

Read More »