Chaotic Student Psychology based Optimization Algorithm for Bi-Objective Permutation Flowshop Scheduling Problem

Abstract

This paper proposes an efficient optimization algorithm called Chaotic Student Psychology Based Optimization (CSPBO) to solve bi-objective permutation flowshop scheduling problem (BPFSP). The SPBO algorithm does not require any tunning parameters which makes it simpler in computational experiments. The original SPBO classifies students related to their efforts in improving their performances. In this paper, we use chaotic maps to enhance student efforts in each category and present different strategic approaches. Logistic, iterative, sine, tent, and singer maps are integrated using proposed strategies to find the best map and also its strategy, named CSPBO. To prove its performance, the CSPBO is compared with several well-known metaheuristic algorithms. The comparison results are evaluated in regard to mean, standard deviation, best terms and ARPD. Since BPFSP is an NP-hard problem, computational experiments on BPFSP instances are carried out according to type size of the dataset. Three types of dataset, small, medium and large are tested. Based on the experiment result, the SPBO integrated with logistic map has significantly given better performance compared to other chaotic maps. Furthermore, CSPBO shows compatible performance compared with other metaheuristic algorithms in solving BPFSP.