TREBLE SEARCH OPTIMIZER: A STOCHASTIC OPTIMIZATION TO OVERCOME BOTH UNIMODAL AND MULTIMODAL PROBLEMS

Abstract

Today, many metaheuristics have used metaphors as their inspiration and baseline for novelty. It makes the novel strategy of these metaheuristics difficult to investigate. Moreover, many metaheuristics use high iteration or swarm size in their first introduction. Based on this consideration, this work proposes a new metaheuristic free from metaphor. This metaheuristic is called treble search optimizer (TSO), representing its main concept in performing three searches performed by each member in each iteration. These three searches consist of two directed searches and one random search. Several seeds are generated from each search. Then, these searches are compared with each other to find the best seed that might substitute the current corresponding member. TSO is also designed to overcome the optimization problem in the low iteration or swarm size circumstance. In this paper, TSO is challenged to overcome the 23 classic optimization functions. In this experiment, TSO is compared with five shortcoming metaheuristics: slime mould algorithm (SMA), hybrid pelican komodo algorithm (HPKA), mixed leader-based optimizer (MLBO), golden search optimizer (GSO), and total interaction algorithm (TIA). The result shows that TSO performs effectively and outperforms these five metaheuristics by making better fitness scores than SMA, HPKA, MLBO, GSO, and TIA in overcoming 21, 21, 23, 23, and 17 functions, consecutively. The result also indicates that TSO performs effectively in overcoming unimodal and multimodal problems in the low iteration and swarm size.