Category: Bio-​inspired optimization algorithms for machine learning

Introduction of ML in agriculture applications and BIOA applicable in ML. Classification plays an important role in ML and deep learning. While working with huge data, there is a need of optimization. The motivated and gave guidelines to the readers to apply BIOAs in ML to solve various optimization problems

Hamed Shah Hosseini proposed intelligent water drops (IWD) algorithm in 2007. It is a revolutionary approach focused on population. It is enthused by the natural river system processes which constitute the actions that occur between flow of river water and the environmental changes in which the river flows. It has two important properties: The environment…

The bacterial foraging optimization algorithm was proposed in 2002 by Passino. This algorithm comprises mainly three mechanisms called chemo taxis, reproduction and elimination–​dispersal. Bacteria get together in the nutrient-​rich areas in an unstructured manner called chemo taxis. In reproduction, superlative personalized bacteria survive and spread their genetic characteristics to next population. Some part of bacteria…

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…