Hybridized Metaheuristic Search Algorithm with Modified Initialization Scheme for Global Optimization

Abstract

Metaheuristic search algorithms (MSAs) such as particle swarm optimization (PSO) and differential evolution (DE) have gained significant attentions from practitioners to solve different engineering applications. Nevertheless, these MSAs are only effective to solve certain problem categories due to No Free Lunch Theorem. A modified hybrid algorithm namely chaotic oppositional based hybridized DE with PSO (CO-HDEPSO) is proposed to address the aforementioned drawbacks in order to solve different global optimization problems more effectively. The chaotic oppositional based initialization scheme is first designed into CO-HDEPSO, aiming to produce initial population with better quality. A hybridization framework is introduced in CO-HDEPSO to combine the strengths of PSO and DE simultaneously for further optimization performance enhancement. Comprehensive analyses are performed to compare the performances of CO-HDEPSO with five MS algorithms using CEC 2014 benchmark functions. The proposed CO-HDEPSO is reported to demonstrate more competitive search accuracy than its peer algorithms in most benchmark functions and able to solve 26 out of 30 CEC 2014 test functions with best Fmean values.KeywordsChaotic systemDifferential evolution (DE)HybridizationOpposition-based learningParticle swarm optimization (PSO)