A NOVEL HYBRID METAHEURISTIC OPTIMIZATION SEARCH TECHNIQUE: MODERN METAHEURISTIC ALGORITHM FOR FUNCTION MINIMIZATION

This paper proposes a novel hybrid metaheuristic optimization search tech-nique named the modern metaheuristic algorithm (MoMA) as one of the most powerful hybrid metaheuristic optimizers. The proposed MoMA combines with two types of the random process drawn from the uniform distribution and the L ' evy distribution to gen-erate the elite solutions. In addition, the automatic adjustable search radius mechanism (ASRM) is conducted in the proposed MoMA to balance the intensification (exploitation) and diversification (exploration) properties and speed up the search process. To validate its search performance, the proposed MoMA is tested against ten selected benchmark optimization problems for minimization. Results obtained by the proposed MoMA are compared with those obtained by the genetic algorithm (GA), particle swarm optimiza-tion (PSO) and cuckoo search (CS). From experimental results, it was found that the proposed MoMA is superior to GA, PSO and CS for function minimization, significant-ly.