A parallel hybrid metaheuristic for the single machine scheduling problem

Mînzu, Viorel; Beldiman, Liviu

doi:10.1109/isatp.2003.1217200

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…In order to solve the problem of work planning, assignment of workers, Tao et al [49] propose a Petri network modeling method and a hybrid genetic algorithm and simulated annealing algorithm (GASA). Minzu and Beldiman [50], [51] propose a meta-heuristic hybrid based on the genetic algorithm and the Kangoroo algorithm to solve the single-machine scheduling problem. Similarly, in 2007, the latter used this hybridization to present a discrete optimization system, solving two real problems in the manufacturing domain: single-machine scheduling problem and workstation task assignment problem [51].…”

Section: Hybrid Genetic Algorithmmentioning

confidence: 99%

Artificial Intelligence for Improving the Optimization of NP-Hard Problems: A Review

2020

IJATCSE

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Artificial intelligence (AI) techniques play an important role in the development and optimization of complex problems in various fields. This study presents a systematic analysis of previously publications on the use of AI techniques to optimize the NP-hard problems in the area of human resource assignment to multi-constrained activities in order to increase the productivity and performance of a company, and in the area of vehicle routing in order to cost minimization. These two problems belonged to the NP-hard family. Among these AI techniques used for a better optimization of this type of problems, we discuss the genetic algorithms (GA), hybrid GA, parallel GA, GA with Big Data and improved AI techniques with special features are also discussed and presented through the different contributions of the researchers.

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Section: Hybrid Genetic Algorithmmentioning

confidence: 99%

Artificial Intelligence for Improving the Optimization of NP-Hard Problems: A Review

2020

IJATCSE

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“…In [6], an efficient GPSO algorithm is proposed in order to get an optimized point without getting trapped in the local optimal point as happened with ordinary PSO. A fast optimization method is essential in systems such as system scheduling [7,8] and human motion tracking [9,10]. The GPSO is said to be able to reach the optimal results in several iterations, which is faster than PSO.…”

Section: 10 Introductionmentioning

confidence: 99%

An Improved Global Particle Swarm Optimization for Faster Optimization Process

2015

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An efficient Global Particle Swarm Optimization (GPSO) is proposed in order to overcome the concern of trapping in the local optimal point especially in high dimensional while using ordinary Particle Swarm Optimization (PSO). GPSO is able to bring all the particles to be closely clumped together faster than PSO. In this paper, an improved GPSO is proposed in order to get a closely clumped particles group faster than using GPSO. The original GPSO is improved by taking into account the global best fitness error and particle fitness clumping size of every iteration. The improved GPSO is simulated by using several two dimension mathematical function and benchmarked with the original GPSO. The improved GPSO is shown to be able to obtain closely clumped particles much more faster than the original GPSO up to 62%. The performances are also evaluated by comparing the standard deviation, average, best particle and worst particles obtained through a 50 independent runs. In term of the four factors mentioned, the improved GPSO performance is shown to be as good of the original GPSO.

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“…The authors have already proposed in [15] a parallel hybrid metaheuristic for solving problems as SMSP. The study presented in that paper was dedicated to this problem and the implementation of the proposed hybrid system was in an early stage.…”

Section: Introductionmentioning

confidence: 99%

Some aspects concerning the implementation of a parallel hybrid metaheuristic

Mînzu

Beldiman

(ISATP 2005). The 6th IEEE International Symposium on Assembly and Task Planning: From Nano to Macro Assembly and Manufacturing

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This paper presents a discrete optimization system implementing a parallel hybrid metaheuristic. This is obtained by joining a genetic algorithm and a parallel version of a stochastic descent method called Kangaroo. The system has the exploration power of the genetic algorithm and the intensification ability of the Kangaroo algorithm. Because the genetic algorithm generates a population of solutions and the Kangaroo algorithm treats a solution at a time, a parallel version of this one was considered. The TWA problem was solved using the proposed metaheuristic. This offered the opportunity to underline some aspects regarding the implementation of this hybrid system. The impact of the precedence constraints upon the implementation of the genetic operators (crossover and mutation) is also considered.

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A parallel hybrid metaheuristic for the single machine scheduling problem

Cited by 4 publications

References 8 publications

Artificial Intelligence for Improving the Optimization of NP-Hard Problems: A Review

Artificial Intelligence for Improving the Optimization of NP-Hard Problems: A Review

An Improved Global Particle Swarm Optimization for Faster Optimization Process

Some aspects concerning the implementation of a parallel hybrid metaheuristic

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