Author: Haroon Khalil

The foraging behavior and mating behaviors of bees are the motivation for artificial bee colony algorithm. A bee chooses a food source by waiting for a decision in dance area and is called onlooker. Some bees visit the food source before calling employed bees. Random search is performed by scout bees to find new sources…

Dorigo and Di Caro predicted this algorithm in 1999, which is one of the most popular SBAs. It is a meta-​heuristic algorithm that is inspired by ants’ forestry behavior, called stigmergy. It enables indirect contact between self-​organizing growing systems by moving individuals across their local environment. It depends on the collaborative actions of group of…

In the year 1995, Kennedy and Eberhart proposed the particle swarm optimization (PSO) technique. This algorithm is motivated by the common behavior of bird groups for food searching. In PSO, the members without mass and without volume depending on the velocities and accelerations in the direction of the best mode of behavior are called particles. Each…

This optimization algorithm is motivated by organism’s collective social behavior. This involves the implementation as a collective intelligence problem-​solving method of simple agent groups focused on the real-​world insect swarm actions. The word “swarm” is used to describe the particles “uneven movements in problem space”. By considering five fundamental principles, swarm intelligence can be described…

Premaratne proposed paddy field algorithm in 2009. It functions on principle of reproduction depending on closeness to the population density and global solution. It is analogous to population of plants. It uses pollination and dispersal strategy. Paddy field algorithm comprises five basic steps called sowing, selection, seeding, pollination and dispersion. This algorithm starts by scattering…

Storn and Price in 1995 projected differential evolution (DE) algorithm, which belongs to EAs [11]. It is similar to genetic algorithm as for optimal solution searches the individual populations are used. In DE, arithmetic combinations of individuals are called mutation, whereas in genetic algorithm small modifications to an individual’s genes are called mutations. Hence, DE mutation…

This is developed through natural selection as inspired from the theory of adaptation and evolution. It deals with micro-​ or genomic-​level data. It is a global optimization algorithm for regulating the distribution of mutations, it utilizes self-​adaptive mechanisms. These self-​adaptive mechanisms involve search progress optimization by developing solutions for the problem and also a few…

In 1992, Koza proposed genetic programming. It is an expansion to genetic algorithm. Genetic programming characterizes a tree-​type non-​direct encoding of a probable solution, and computer program might be used in which search is applied directly to the solution. Genetic programming takes up variable-​length representation, whereas fixed-​length encoding is adopted by genetic algorithm. In genetic…

In 1975, Holland proposed a genetic algorithm which is a stochastic algorithm based on evolution for optimization. This algorithm follows the theory of the “survival of the fittest” proposed by Charles Darwin. First, a solution population called chromosomes is initialized. It represents the problem in the bit vector form. Then the fitness of every chromosome is…

Evolutionary computation is a concept in ML whose main objective is to increase the gain knowledge from the phenomena of collectiveness in adaptive population for problem-​solvers utilizing the iterative progress including selection, growth, development, reproduction and survival as in population. EAs are algorithms of optimization which are nondeterministic or cost based. Genetic algorithm, genetic programming,…