Using Non-dominated Sorting Particle Swarm Optimization Algorithm II for Bi-objective Flow Shop Scheduling Problems

Abstract

A hybrid particulate swarm optimization (hybrid) combination of an optimization algorithm of the particle swarm and a variable neighborhood search algorithm is proposed for the multi-objective permutation flow shop scheduling problem (PFSP) with the smallest cumulative completion time and the smallest total flow time. Algorithm for hybrid particulate swarm optimization (HPSO) is applied to maintain a fair combination of centralized search with decentralized search. The Nawaz-Enscore-Ham )NEH) heuristic algorithm in this hybrid algorithm is used to initialize populations in order to improve the efficiency of the initial solution. The method design is based on ascending order (ranked-order-value, ROV), applying the continuous PSO algorithm to the PFSP, introducing the external archive set storage Pareto solution, and using a hybrid strategy that combines strong dominance and aggregation distance to ensure the distribution of the solution set. We adopted the Sigma method and the roulette method, based on the aggregation distance, to select the global optimal solution. A variable neighborhood search algorithm was proposed to further search the Pareto solution in the external set. The suggested hybrid algorithm was used to solve the Taillard test set and equate the test results with the SPEA2 algorithm to check the scheduling algorithm’s efficacy.

Minimizing Total Completion Time, Total Earliness Time, and Maximum Tardiness for a Single Machine scheduling problem

“…Dominance Rules (DRs) are used efficiently in reducing the current sequences [23][24][25]. DR is used usually to indicate whether a certain node in a BAB method can be eliminated before calculating its lower bound.…”

Genetic Algorithm and Particle Swarm Optimization Techniques for Solving Multi-Objectives on Single Machine Scheduling Problem

“…propose the (PSO) and the (GA) as heuristic methods to find approximation solutions for ∑ ∑ and they found that these local search algorithms solve the problem for jobs with reasonable time. The (PSO) algorithm were applied by Hanan [5]. For solving the problem ∑…”

“…The performance of the (BAB) procedure is compared on 5 problem instances, the sizes of these examples are n = [5,18]. The problem instances were generated randomly, and for each job where , the processing time was uniformly generated in [1,10].…”

Community Detection in Modular Complex Networks Using an Improved Particle Swarm Optimization Algorithm

“…PSO appoints a set of particles that explore the solutions by moving locally and globally in the search landscape to identify the optimal solution. The movement strategy of the employed particles is inspired by the movement mechanism of a bird swarm, where each particle saves its coordinate path in the search space and correlates with the best captured local and global solution (i.e., local and global optima) to the swarm [3,23,24]. To identify the best solution, the movement of each particle will direct toward the obtained local solution as well as the global solution.…”