JOURNAL ARTICLE

Hybrid Differential Evolution - Particle Swarm Optimization Algorithm for Solving Global Optimization Problems

Abstract

This paper presents a simple, hybrid two phase global optimization algorithm called DE-PSO for solving global optimization problems. DE-PSO consists of alternating phases of differential evolution (DE) and Particle Swarm Optimization (PSO). The algorithm is designed so as to preserve the strengths of both the algorithms. Empirical results show that the proposed DE-PSO is quite competent for solving the considered test functions as well as real life problems.

Keywords:
Multi-swarm optimization Particle swarm optimization Meta-optimization Imperialist competitive algorithm Metaheuristic Differential evolution Mathematical optimization Global optimization Computer science Derivative-free optimization Optimization problem Simple (philosophy) Algorithm Test functions for optimization Mathematics

Metrics

41
Cited By
2.79
FWCI (Field Weighted Citation Impact)
15
Refs
0.94
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Metaheuristic Optimization Algorithms Research
Physical Sciences →  Computer Science →  Artificial Intelligence
Evolutionary Algorithms and Applications
Physical Sciences →  Computer Science →  Artificial Intelligence
Advanced Multi-Objective Optimization Algorithms
Physical Sciences →  Computer Science →  Computational Theory and Mathematics
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