Particle Swarm Optimization for Continuous Function Optimization Problems

Abstract

In this paper, particle swarm optimization is proposed for finding the global minimum of continuous functions and tested on benchmark problems. Particle swarm optimization applied on 21 benchmark test functions, and its solutions are compared to those former proposed approaches: ant colony optimization, a heuristic random optimization, the discrete filled function algorithm, an adaptive random search, dynamic random search technique and random selection walk technique. The implementation of the PSO on several test problems is reported with satisfactory numerical results when compared to previously proposed heuristic techniques. PSO is proved to be successful approach to solve continuous optimization problems.

Parallel Algorithm on Multicore Processor and Graphics Processing Unit for the Optimization of Electric Vehicle Recharge Scheduling

“…The algorithm proposed is a multi-run PSO, which addresses the nondeterministic aspect of metaheuristics and provides a higher-quality result than a single-run PSO. Compared to other metaheuristics, the PSO is a natural fit for continuous optimization functions as particles evolve in a continuous multidimensional space [20]. To address the long execution time of the metaheuristic, the algorithm is parallelized on CPUs using task-level parallelization and on GPUs using data-level parallelization, and the performance results of both methods are thoroughly compared.…”

Using particle swarm optimization to solve test functions problems

“…They analyzed the method from various views of that are the structure, parameters, discrete PSO, parallel PSO, and multiobjective. Ozdemir [6] used the PSO to reach to the global minimum of the function suggested. He applied the method on different benchmark functions.…”