An improved island model memetic algorithm with a new cooperation phase for multi-objective job shop scheduling problem

Kurdi, Mohamed Zakaria

doi:10.1016/j.cie.2017.07.021

Cited by 34 publications

(15 citation statements)

References 68 publications

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“…The differential evolution (DE) proposed by Storn and Price [34], is a stochastic search algorithm and has been successfully applied to solve optimization problems. The differential mutation operation is defined by Equation 19. The calculation formula of differential mutation operation is as follows…”

Section: 21mentioning

confidence: 99%

“…There are many researchers made use one of the approximation methods, metaheuristic optimization algorithms [29], such as, tabu search algorithm [28,30], genetic algorithm (GA) [31,40,21,37], [17], simulated annealing (SA) [3,41], ant colony optimization (ACO) [36,39], particle swarm optimization (PSO) [20,22], bat algorithm [9], discrete PSO algorithm [32], genetic PSO algorithm [1], local search algorithm [26], island model genetic algorithm [18], asexual genetic algorithm [2], island model memetic algorithm [19], Ant System parameter interactions method [10], neighborhood structure evaluation method [12], adaptive & opposite genetic algorithm [24], metaheuristic algorithm for the JSSP [29]. The computational results show that the metaheuristic optimization algorithm can produce optimal or near-optimal values on almost all tested benchmark instances.…”

mentioning

confidence: 99%

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

Zhou¹,

Zhou²,

Zhao³

2021

Journal of Industrial &Amp; Management Optimization

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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.

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Section: 21mentioning

confidence: 99%

mentioning

confidence: 99%

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

Zhou¹,

Zhou²,

Zhao³

2021

Journal of Industrial &Amp; Management Optimization

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“…In the operating system Windows Vista Ultimate 64 bits, the threads are administrated like objects, which are created for calling functions to the applications programming interface (API), using the MFC library [29]. In the SACT algorithm, the functions of the MFC of Microsoft visual C++, 2017, are used for the creation of the threads in a single process.…”

Section: Simulated Annealing Algorithm With Cooperative Threads (Sact)mentioning

confidence: 99%

“…The scheduling of the jobs in each of the problem's machines can be observed. This type of distribution in the scheduling is characteristic of all problems, TA (21)(22)(23)(24)(25)(26)(27)(28)(29)(30), YN (1)(2)(3)(4), and DMU (06-10). The DMU problems (49-50) show different behavior.…”

Section: Sact Statistic Reviewmentioning

confidence: 99%

Cooperative Threads with Effective-Address in Simulated Annealing Algorithm to Job Shop Scheduling Problems

Cruz-Chávez

Peralta-Abarca²,

Cruz-Rosales

2019

Applied Sciences

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This paper presents a parallel algorithm applied to the job shop scheduling problem (JSSP). The algorithm generates a set of threads, which work in parallel. Each generated thread, executes a procedure of simulated annealing which obtains one solution for the problem. Each solution is directed towards the best solution found by the system at the present, through a procedure called effective-address. The cooperative algorithm evaluates the makespan for various benchmarks of different sizes, small, medium, and large. A statistical analysis of the results of the algorithm is presented and a comparison of performance with other (sequential, parallel, and distributed processing) algorithms that are found in the literature is presented. The obtained results show that the cooperation of threads carried out by means of effective-address procedure permits to simulated annealing to work with increased efficacy and efficiency for problems of JSSP.

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“…This is maybe due to the fact that it is difficult to identify the genetic materials that hold the important traits of an individual. However, a recent study, made by Kurdi [23], has recommended identifying the important genetic materials according to some criteria and giving them the preference for exchange over the rest. In his work, he proposed an informed uniform crossover that employs the history of parents' evolution occurred during the self-adaptation phase (local search via TS) in determining the genetic materials exchanged between them i.e.…”

Section: Introductionmentioning

confidence: 99%

An effective genetic algorithm with a critical-path-guided Giffler and Thompson crossover operator for job shop scheduling problem

Kurdi¹

2019

ijisae

Self Cite

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This work presents an effective genetic algorithm with a critical-path-guided Giffler and Thompson crossover operator for job shop scheduling problem with the objective of makespan minimization (GA-CPG-GT). Even though passing important traits from parents to offspring is known to be an important feature of any uniform crossover operator, most of the proposed operators adopt random exchange of genetic materials between parents; this is probably due to the fact that it is tricky to identify the genetic materials that hold the important traits. For that reason, in this work, a new selective exchange of genetic materials is proposed. In the proposed approach, at first, the genetic materials that hold the important traits are identified according to some domain specific information provided by the critical paths of the parents, and then the exchange is made on favor of them. The properties of critical path are usually utilized by the local search methods such as tabu search and simulating annealing; however, in this work, they are utilized in the global search method GA during the crossover operator. The implications of the proposed approach are investigated using the Giffler and Thompson crossover operator, which is a uniform crossover combined with the G&T algorithm. The proposed approach is tested on 55 benchmark instances, with the proposed selective exchange, and without it using the random one, and also compared with other 5 similar works reported in the literature. The computational results validate the enhancements accomplished by the proposed selective exchange, and show the superiority of the proposed algorithm over the compared works in terms of solution quality, and validate its effectiveness.

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An improved island model memetic algorithm with a new cooperation phase for multi-objective job shop scheduling problem

Cited by 34 publications

References 68 publications

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

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

Cooperative Threads with Effective-Address in Simulated Annealing Algorithm to Job Shop Scheduling Problems

An effective genetic algorithm with a critical-path-guided Giffler and Thompson crossover operator for job shop scheduling problem

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