A Self-adaptive Hybrid Bio-inspired Optimization Algorithm by Using Sigmoidal Function

Abstract

The article presents a new hybrid algorithm, which designs based on traditional bio-inspired optimization algorithms. The algorithm leverages the advantage of Particle Swarm Optimization (PSO), Differential Evolution (DE), and Artificial Bee Colony (ABC), replacing other algorithm weaknesses. A new algorithm we proposed is the Fast bio-inspired Optimization Algorithm (FOA). The DE uses multi-parent for trial vector calculation. It increases the diversity of the solution, while the sigmoidal function adds a self-adaptive characteristic to the proposed algorithm. The function replaces a weighting scheme of PSO. In sub-optimal avoidance, the FOA includes a scout bee behavior from ABC. It makes FOA providing the solution faster than traditional versions, while the solution quality is maintained at an acceptable level. According to a new design, an FOA can reduce the algorithm runtime up to 43.57%, 37.14%, 40.78%, and 31.30% compared to PSO, DE, ABC, and DEPSO, respectively. The DEPSO is the hybrid algorithm between DE and PSO. The best solution to FOA is better than the traditional version of the algorithms. The new algorithm design and the optimization speed improvement are the highlight contribution of this article.