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

Purba Daru Kusuma; Ashri Dinimaharawati

doi:10.31436/iiumej.v24i2.2700

Authors

Purba Daru Kusuma Telkom University https://orcid.org/0000-0001-5973-5229
Ashri Dinimaharawati Telkom University

DOI:

https://doi.org/10.31436/iiumej.v24i2.2700

Keywords:

optimization, Metaheuristics, swarm intelligence, unimodal, multimodal

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.

ABSTRAK: Dewasa ini, terdapat ramai metaheuristik menggunakan metafora sebagai inspirasi dan garis dasar pembaharuan. Ini menyebabkan strategi baharu metaheuristik ini susah untuk dikaji. Tambahan, ramai metaheuristik menggunakan ulangan berulang atau saiz kerumunan dalam pengenalan mereka. Berdasarkan penilaian ini, kajian ini mencadangkan metaheuristk baharu bebas metafora. Metaheuristik ini dipanggil pengoptimum pencarian ganda tiga (TSO), mewakilkan konsep utama dalam pemilihan tiga pencarian yang dilakukan oleh setiap ahli dalam setiap ulangan. Ketiga-tiga carian ini terdiri daripada dua pencarian terarah dan satu pencarian rawak. Beberapa benih dihasilkan dalam setiap carian. Kemudian, carian ini dibandingkan antara satu sama lain bagi mencari benih terbaik yang mungkin berpotensi menggantikan ahli yang sedang digunakan. TSO juga direka bagi mengatasi masalah pengoptimuman dalam ulangan rendah atau lingkungan saiz kerumunan. Kajian ini TSO dicabar bagi mengatasi 23 fungsi pengoptimuman klasik. Eksperimen ini TSO dibandingkan dengan lima kekurangan metaheuristik: algoritma acuan lendir (SMA), algorithma hibrid komodo burung undan (HPKA), Pengoptimum Campuran berdasarkan-Ketua (MLBO), Pengoptimuman Carian Emas (GSO), dan algoritma jumlah interaksi (TIA). Dapatan kajian menunjukkan TSO berkesan menghasilkan dan lebih baik daripada kelima-lima metaheuristik dengan menghasilkan pemarkahan padanan terbaik berbanding SMA, HPKA, MLBO, GSO, dan TIA dalam mengatasi fungsi 21, 21, 23, 23, dan 17, secara berurutan. Dapatan kajian juga menunjukkan TSO turut berperanan efektif dalam mengatasi masalah modal tunggal dan modal ganda dalam iterasi rendah dan saiz kerumunan.

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