Particle swarm optimization and differential evolution algorithms for continuous optimization problems

Abstract

This study presents Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms to solve nonlinear continuous function optimization problems. The algorithms were tested using 14 newly proposed benchmark instances in Congress on Evolutionary Computation 2005. Particle Swarm Optimization (PSO) and Differential Evolution (DE) are two of the latest metaheuristic methods. PSO is based on the metaphor of social interaction and communication such as bird flocking and fish schooling. PSO and DE were both first introduced to optimize various continuous nonlinear functions. In a PSO algorithm, each member is called a particle, and each particle moves around in the multi-dimensional search space with a velocity constantly updated by the particle?s experience, the experience of the particle?s neighbors, and the experience of the whole swarm. In the DE algorithm, the target population is perturbed with a mutant factor, and the crossover operator is then introduced to combine the mutated population with the target population so as to generate a trial population. Then the selection operator is applied to compare the fitness function value of both competing populations, namely, target and trial populations. The better individuals among these two populations become members of the population for the next generation. This process is repeated until a convergence occurs. The computational results show that the particle swarm optimization is able to solve the test problems. Both algorithms are promising to solve benchmark problems. However, the differential evolution algorithm performed better for the larger size of problems than the particle swarm optimization algorithm.