A hybrid Particle Swarm Optimization – Variable Neighborhood Search algorithm for Constrained Shortest Path problems

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.

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A New Hybrid Particle Swarm Optimization with Variable Neighborhood Search for Solving Unconstrained Global Optimization Problems

Advances in Intelligent Systems and Computing - Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014 ◽

10.1007/978-3-319-08156-4_16 ◽

2014 ◽

pp. 151-160 ◽

Cited By ~ 4

Author(s):

Ahmed Fouad Ali ◽

Aboul Ella Hassanien ◽

Václav Snášel ◽

Mohamed F. Tolba

Keyword(s):

Global Optimization ◽

Particle Swarm Optimization ◽

Variable Neighborhood Search ◽

Optimization Problems ◽

Particle Swarm ◽

Neighborhood Search ◽

Swarm Optimization ◽

Hybrid Particle ◽

Hybrid Particle Swarm Optimization

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A hybrid particle swarm optimization with a variable neighborhood search for the localization enhancement in wireless sensor networks

Applied Intelligence ◽

10.1007/s10489-019-01467-8 ◽

2019 ◽

Vol 49 (10) ◽

pp. 3539-3557 ◽

Cited By ~ 11

Author(s):

Bassam Faiz Gumaida ◽

Juan Luo

Keyword(s):

Wireless Sensor Networks ◽

Particle Swarm Optimization ◽

Sensor Networks ◽

Variable Neighborhood Search ◽

Particle Swarm ◽

Wireless Sensor ◽

Neighborhood Search ◽

Swarm Optimization ◽

Hybrid Particle ◽

Hybrid Particle Swarm Optimization

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A new hybrid particle swarm optimization and parallel variable neighborhood search algorithm for flexible job shop scheduling with assembly process

Assembly Automation ◽

10.1108/aa-11-2018-0178 ◽

2020 ◽

Vol 40 (3) ◽

pp. 419-432 ◽

Cited By ~ 2

Author(s):

Parviz Fattahi ◽

Naeeme Bagheri Rad ◽

Fatemeh Daneshamooz ◽

Samad Ahmadi

Keyword(s):

Particle Swarm Optimization ◽

Variable Neighborhood Search ◽

Job Shop ◽

Particle Swarm ◽

Neighborhood Search ◽

Flexible Job Shop ◽

Flexible Job Shop Scheduling ◽

Swarm Optimization ◽

Content Type ◽

Hybrid Particle Swarm Optimization

Purpose The purpose of this paper is to present a mathematical model and a new hybrid algorithm for flexible job shop scheduling problem with assembly operations. In this problem, each product is produced by assembling a set of several different parts. At first, the parts are processed in a flexible job shop system, and then at the second stage, the parts are assembled and products are produced. Design/methodology/approach As the problem is non-deterministic polynomial-time-hard, a new hybrid particle swarm optimization and parallel variable neighborhood search (HPSOPVNS) algorithm is proposed. In this hybrid algorithm, particle swarm optimization (PSO) algorithm is used for global exploration of search space and parallel variable neighborhood search (PVNS) algorithm for local search at vicinity of solutions obtained in each iteration. For parameter tuning of the metaheuristic algorithms, Taguchi approach is used. Also, a statistical test is proposed to compare the ability of metaheuristics at finding the best solution in the medium and large sizes. Findings Numerical experiments are used to evaluate and validate the performance and effectiveness of HPSOPVNS algorithm with hybrid particle swarm optimization with a variable neighborhood search (HPSOVNS) algorithm, PSO algorithm and hybrid genetic algorithm and Tabu search (HGATS). The computational results show that the HPSOPVNS algorithm achieves better performance than competing algorithms. Practical implications Scheduling of manufacturing parts and planning of assembly operations are two steps in production systems that have been studied independently. However, with regard to many manufacturing industries having assembly lines after manufacturing stage, it is necessary to deal with a combination of these problems that is considered in this paper. Originality/value This paper proposed a mathematical model and a new hybrid algorithm for flexible job shop scheduling problem with assembly operations.

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