Particle swarm optimization (PSO) is one of the popular nature-inspired metaheuristic
algorithms. It has been used in different applications. The convergence analysis is among …

In this work greedy comparison between particle swarm optimization and artificial bee
colony algorithms was made using different test functions. Each algorithm was explained in …

Based on the analysis of the basic schemes of a variety of population-based metaheuristics
(PBMH), the main components of PBMH are described with functional relationships in this …

Multipopulation methods are effective in solving dynamic optimization problems. However,
to efficiently track multiple optima, algorithm designers need to address a key issue: how to …

Artificial bee colony (ABC) optimization algorithm is relatively a simple and recent population
based probabilistic approach for global optimization. ABC has been outperformed over …

The population initialization step is a common step in the majority (or even all) of
evolutionary algorithms (EAs). There are many population initialization techniques. Due to …

In this paper, a variant of the particle swarm optimisation (PSO) algorithm is introduced with
heterogeneous behaviour and a new dynamic multi-swarm topological structure. The new …

PARTICLE swarm optimization (PSO) is one of the evo-lutionary computation techniques. It
is a population-based search algorithm and is initialized with a population of random …

Particle Swarm Optimization (PSO) has been frequently employed to solve diversified
optimization problems. Choosing initial placement for population plays an important role in …

This paper proposes a new version of the Artificial Bee Colony (ABC) optimization algorithm
based on the natural nest-site selection behavior of honey bee swarms. The algorithm offers …