HEPSO: High exploration particle swarm optimization

Particle swarm optimization (PSO) is a heuristic optimization technique which was inspired by flocking and swarming behavior of birds and insects. Same as other swarm intelligent methods, this algorithm also has its own disadvantages, such as premature convergence and rapid loss of diversity. In this paper, a new optimization method based on the combination of PSO and two novel operators is introduced in order to increase the exploration capability of the PSO algorithm (HEPSO). The first operator is inspired by the multi-crossover mechanism of the genetic algorithm, and the second operator uses the bee colony mechanism to update the position of the particles. Various values for probabilities are examined to find a trade-off for the PSO, multi-crossover formulation, and bee colony operator. The performance of the hybrid algorithm is tested using several well-known benchmark functions. The comparative study confirms that HEPSO is a promising global optimization algorithm and superior to the recent variants of PSO in terms of accuracy, speed, robustness, and efficiency.