A Hybrid Genetic Algorithm and Particle Swarm Optimization for Flow Shop Scheduling Problems

Alvarez-Pomar, Lindsay; Pulido, Elizabeth Cruz; Roa, Julián Darío Tovar

doi:10.1007/978-3-319-66963-2_53

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…Usually, the main idea of the hybrid optimization algorithm can be considered as applying a local search approach with the genetic algorithm operations such as crossover and mutation in one procedure (Pomar et al, 2017;Moin et al 2015). This section summarizes the structure of our proposed algorithm (denoted by HGASSO).…”

Section: Hybrid Genetic Algorithm and Sperm Swarm Optimization (Hgasso)mentioning

confidence: 99%

A Hybrid Genetic Algorithm and Sperm Swarm Optimization (HGASSO) for Multimodal Functions

Shehadeh

Mustafa

Tubishat

2022

International Journal of Applied Metaheuristic Computing

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In this paper, we propose a hybrid algorithm combining two different metaheuristic methods, “Genetic Algorithms (GA)” and “Sperm Swarm Optimization (SSO)”, for the global optimization of multimodal benchmarks functions. The proposed Hybrid Genetic Algorithm and Sperm Swarm Optimization (HGASSO) operates based on incorporates concepts from GA and SSO in which generates individuals in a new iteration not only by crossover and mutation operations as proposed in GA, but also by techniques of local search of SSO. The main idea behind this hybridization is to reduce the probability of trapping in local optimum of multi modal problem. Our algorithm is compared against GA, and SSO metaheuristic optimization algorithms. The experimental results using a suite of multimodal benchmarks functions taken from the literature have evinced the superiority of the proposed HGASSO approach over the other approaches in terms of quality of results and convergence rates in which obtained good results in solving the multimodal benchmarks functions that include cosine, sine, and exponent in their formulation.

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Section: Hybrid Genetic Algorithm and Sperm Swarm Optimization (Hgasso)mentioning

confidence: 99%

A Hybrid Genetic Algorithm and Sperm Swarm Optimization (HGASSO) for Multimodal Functions

Shehadeh

Mustafa

Tubishat

2022

International Journal of Applied Metaheuristic Computing

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“…It has received extensive attention from researchers. A large number of research results have appeared [8][9][10][11][12][13][14][15][16][17][18][19]. However, in these studies, the objective function of the problem is rarely to minimize carbon emissions or total energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Modified Multi-Crossover Operator NSGA-III for Solving Low Carbon Flexible Job Shop Scheduling Problem

et al. 2020

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Low carbon manufacturing has received increasingly more attention in the context of global warming. The flexible job shop scheduling problem (FJSP) widely exists in various manufacturing processes. Researchers have always emphasized manufacturing efficiency and economic benefits while ignoring environmental impacts. In this paper, considering carbon emissions, a multi-objective flexible job shop scheduling problem (MO-FJSP) mathematical model with minimum completion time, carbon emission, and machine load is established. To solve this problem, we study six variants of the non-dominated sorting genetic algorithm-III (NSGA-III). We find that some variants have better search capability in the MO-FJSP decision space. When the solution set is close to the Pareto frontier, the development ability of the NSGA-III variant in the decision space shows a difference. According to the research, we combine Pareto dominance with indicator-based thought. By utilizing three existing crossover operators, a modified NSGA-III (co-evolutionary NSGA-III (NSGA-III-COE) incorporated with the multi-group co-evolution and the natural selection is proposed. By comparing with three NSGA-III variants and five multi-objective evolutionary algorithms (MOEAs) on 27 well-known FJSP benchmark instances, it is found that the NSGA-III-COE greatly improves the speed of convergence and the ability to jump out of local optimum while maintaining the diversity of the population. From the experimental results, it can be concluded that the NSGA-III-COE has significant advantages in solving the low carbon MO-FJSP.

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A Hybrid Genetic Algorithm and Particle Swarm Optimization for Flow Shop Scheduling Problems

Cited by 2 publications

References 12 publications

A Hybrid Genetic Algorithm and Sperm Swarm Optimization (HGASSO) for Multimodal Functions

A Hybrid Genetic Algorithm and Sperm Swarm Optimization (HGASSO) for Multimodal Functions

Modified Multi-Crossover Operator NSGA-III for Solving Low Carbon Flexible Job Shop Scheduling Problem

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