A Modified Genetic Algorithm with Local Search Strategies and Multi-Crossover Operator for Job Shop Scheduling Problem

Viana, Monique Simplicio; Morandin, Orides; Contreras, Rodrigo Colnago

doi:10.3390/s20185440

Cited by 40 publications

(28 citation statements)

References 65 publications

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“…ere are many algorithms to solve multiobjective scheduling problems, such as genetic algorithm [17,18], particle swarm algorithm [19,20], migratory bird optimization algorithm [21,22], and FA [23,24]. As a new swarm intelligence optimization method, FA has the advantages of a simple model, less adjustable parameters, easy parallel processing, and fast convergence speed, so it has been applied in many fields [25,26].…”

Section: Improvement Of Fa To Solve the Modelmentioning

confidence: 99%

Integrated Optimization Method of IPPS under TOU and Tiered Electricity Price

Yang

et al. 2021

Discrete Dynamics in Nature and Society

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Energy-saving production is one of the issues that must be paid attention to by today’s manufacturing enterprises. Aiming at the problem of integrated process planning and scheduling (IPPS) in the manufacturing process, considering Time-of-Use (TOU) and tiered electricity price, this paper systematically studies the energy-saving scheduling problem in order to reduce the power consumption in processing and production. To establish the multiobjective optimization mathematical model of the problem, the load balancing problem of the equipment is considered, the minimization of the power consumption and the maximum load of the equipment are taken as the optimization objectives. Then, considering the constraints of resource and multiprocess, the switching strategy of the equipment in idle time is introduced, including shutdown and restart operations. In order to solve the model easily, a multiobjective firefly algorithm (MOFA) based on five-layer coding is designed, and the elite strategy is introduced to protect the excellent firefly individuals in the iterative population. Finally, through a specific example, the Pareto solution set is obtained, which provides a reference scheme for decision-makers, and verifies the correctness of the model and the effectiveness of the algorithm.

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Section: Improvement Of Fa To Solve the Modelmentioning

confidence: 99%

Integrated Optimization Method of IPPS under TOU and Tiered Electricity Price

Yang

et al. 2021

Discrete Dynamics in Nature and Society

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“…The multiobjective optimization problem remains relevant in recent years, especially considering the number of works in the production, engineering, education, and other research fields. Moreover, many such publications provide information on the use of GA, including the analyzed ones [14][15][16] where the researchers apply GA to solve scheduling and similar problems. Amjad et al [14] made a detailed review of recent achievements in the application of GA to solve the flexible job-shop scheduling problem (JSSP).…”

Section: Optimization Algorithms For Solving Multicriteria Problemsmentioning

confidence: 99%

“…Amjad et al [14] made a detailed review of recent achievements in the application of GA to solve the flexible job-shop scheduling problem (JSSP). Viana, Junior, and Contreras [15] proposed a new GA with improved crossover and mutation operators for JSSP. Rarità et al [16] developed a supply chains model through partial and ordinary differential equations.…”

Section: Optimization Algorithms For Solving Multicriteria Problemsmentioning

confidence: 99%

Evaluation of the Forms of Education of High School Students Using a Hybrid Model Based on Various Optimization Methods and a Neural Network

et al. 2021

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This article deals with the multicriteria programming model to optimize the time of completing home assignments by school students in both in-class and online forms of teaching. To develop a solution, we defined 12 criteria influencing the school exercises’ effectiveness. In this amount, five criteria describe exercises themselves and seven others the conditions at which the exercises are completed. We used these criteria to design a neural network, which output influences target function and the search for optimal values with three optimization techniques: backtracking search optimization algorithm (BSA), particle swarm optimization algorithm (PSO), and genetic algorithm (GA). We propose to represent the findings for the optimal time to complete homework as a Pareto set.

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“…To solve these problems, many scholars have performed much research on genetic algorithm. Viana et al [ 16 ] improved the local search strategy in the traditional mutation operator and proposed a new multicrossover operator. This method can solve the premature problem of genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

The Application of an Adaptive Genetic Algorithm Based on Collision Detection in Path Planning of Mobile Robots

Hao

Zhao

Wang

et al. 2021

Computational Intelligence and Neuroscience

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An adaptive genetic algorithm based on collision detection (AGACD) is proposed to solve the problems of the basic genetic algorithm in the field of path planning, such as low convergence path quality, many iterations required for convergence, and easily falling into the local optimal solution. First, this paper introduces the Delphi weight method to evaluate the weight of path length, path smoothness, and path safety in the fitness function, and a collision detection method is proposed to detect whether the planned path collides with obstacles. Then, the population initialization process is improved to reduce the program running time. After comprehensively considering the population diversity and the number of algorithm iterations, the traditional crossover operator and mutation operator are improved, and the adaptive crossover operator and adaptive mutation operator are proposed to avoid the local optimal solution. Finally, an optimization operator is proposed to improve the quality of convergent individuals through the second optimization of convergent individuals. The simulation results show that the adaptive genetic algorithm based on collision detection is not only suitable for simulation maps with various sizes and obstacle distributions but also has excellent performance, such as greatly reducing the running time of the algorithm program, and the adaptive genetic algorithm based on collision detection can effectively solve the problems of the basic genetic algorithm.

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A Modified Genetic Algorithm with Local Search Strategies and Multi-Crossover Operator for Job Shop Scheduling Problem

Cited by 40 publications

References 65 publications

Integrated Optimization Method of IPPS under TOU and Tiered Electricity Price

Integrated Optimization Method of IPPS under TOU and Tiered Electricity Price

Evaluation of the Forms of Education of High School Students Using a Hybrid Model Based on Various Optimization Methods and a Neural Network

The Application of an Adaptive Genetic Algorithm Based on Collision Detection in Path Planning of Mobile Robots

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