Hybrid social spider optimization algorithm with differential mutation operator for the job-shop scheduling problem

Abstract

The job-shop scheduling problem is one of the well-known hardest combinatorial optimization problems. The problem has captured the interest of a significant number of researchers, but no efficient solution algorithm has been found yet for solving it to optimality in polynomial time. In this paper, a hybrid social-spider optimization algorithm with differential mutation operator is presented to solve the job-shop scheduling problem. To improve the exploration capabilities of the social spider optimization algorithm (SSO), we incorporate the DM operator (a mutation operator taken from the deferential evolutionary (DE) algorithm) into the framework of the female cooperative operator. The experimental results show that the proposed method effectiveness in solving job-shop scheduling compared to other optimization algorithms in the literature.2010 Mathematics Subject Classification. Primary: 78M50, 90C27.

A DNA algorithm for the job shop scheduling problem based on the Adleman-Lipton model

“…Table 5 shows a comparative analysis with four more heuristics on four instances of different sizes. The four heuristics are PSO [ 37 ], IGA [ 38 ], DE [ 39 ] and SSO-DM [ 18 ]. The table gives the statistical results, i.e., the best, worst, mean and standard deviation (Std.…”

“…Except for instance YN4, the proposed algorithm found the best known solutions for the remaining three instances. The results of the comparative heuristics are from Zhou et al [ 18 ]. Figs 8 – 11 show the box plots of these five algorithms on the four instances.…”

“…Mohamed Kurdi [ 17 ] proposed an effective genetic algorithm with a critical-path-guided Giffler and Thompson crossover operator (GA-CPG-GT) for JSSP. Zhou et al [ 18 ] presented a hybrid social-spider optimization algorithm with a differential mutation (SSO-DM) operator to solve JSSP. Cruz-Chávez et al [ 19 ] proposed a parallel algorithm that generated a set of parallel working threads, where each thread performed a simulated annealing process to solve JSSP.…”

“…Due to the dependence on random natural selection and recombination, the optimization results obtained by classical evolutionary algorithm are still limited [ 24 ]. Also due to the stubborn nature of JSSP, a single meta-heuristic method can no longer solve this problem well [ 18 ]. In addition, these heuristic approaches do not traverse all possible solutions, and can only find relatively good solutions through operations such as crossover, mutation and iteration.…”

Improved Hybrid Heuristic Algorithm Inspired by Tissue-Like Membrane System to Solve Job Shop Scheduling Problem

“…Abdel-Kader [17] combines standard PSO and GA to solve JSSP. Zhou [18] fuses the social spider optimization algorithm and the differential evolutionary based mutation operator for solving the JSSP. Peng et al [19] proposed a MAGATS algorithm that combines multi-agent GA and TS to solve JSSP.…”