An efficient real-coded genetic algorithm for real-parameter optimization

Chen, Zhiqiang; Wang, Rong-Long

doi:10.1109/icnc.2010.5584209

Cited by 14 publications

(13 citation statements)

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“…26). The direction which a mutant vector will move towards is determined by simulating a flopped unbiased coin (Cormier et al 2001;Chen and Wang 2011). The d tt helps in improving the search ability of GA, its value decreases as number of generation increases (Eq.…”

Section: Meta-heuristicsmentioning

confidence: 99%

An enhanced reliability-oriented workforce planning model for process industry using combined fuzzy goal programming and differential evolution approach

2017

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This paper draws on the ''human reliability'' concept as a structure for gaining insight into the maintenance workforce assessment in a process industry. Human reliability hinges on developing the reliability of humans to a threshold that guides the maintenance workforce to execute accurate decisions within the limits of resources and time allocations. This concept offers a worthwhile point of deviation to encompass three elegant adjustments to literature model in terms of maintenance time, workforce performance and return-on-workforce investments. These fully explain the results of our influence. The presented structure breaks new grounds in maintenance workforce theory and practice from a number of perspectives. First, we have successfully implemented fuzzy goal programming (FGP) and differential evolution (DE) techniques for the solution of optimisation problem in maintenance of a process plant for the first time. The results obtained in this work showed better quality of solution from the DE algorithm compared with those of genetic algorithm and particle swarm optimisation algorithm, thus expressing superiority of the proposed procedure over them. Second, the analytical discourse, which was framed on stochastic theory, focusing on specific application to a process plant in Nigeria is a novelty. The work provides more insights into maintenance workforce planning during overhaul rework and overtime maintenance activities in manufacturing systems and demonstrated capacity in generating substantially helpful information for practice.

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Section: Meta-heuristicsmentioning

confidence: 99%

An enhanced reliability-oriented workforce planning model for process industry using combined fuzzy goal programming and differential evolution approach

2017

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“…In the simulations, blended crossover (Raad, 2011) is used as [X min +a(X max -X min ),X max -a (X max -X min )], where a is a user-defined parameter; x are the i th gen of the first and second individuals chosen for the crossover, respectively. Mutation for this simulation is based on (Chen and Wang, 2011) where gen x i is expressed as follows:…”

Section: Simulation Methodsmentioning

confidence: 99%

Membrane Computing Inspired Genetic Algorithm on Multi-Core Processors

Maroosi

Muniyandi

2013

Journal of Computer Science

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Membrane computing is a branch of natural computing. Several studies have recently attempted to utilize the structure of membrane computing to improve intelligent algorithms. These studies have applied communication rules in membrane models to facilitate information exchange between membranes, thereby improving the performance of those algorithms. However, parallel membrane computing has not yet been considered. This study proposes a membrane computing-inspired genetic algorithm. Similar to previous studies, the algorithm also uses communication rules to facilitate information exchange. In this study, an appropriate membrane computing-inspired genetic algorithm is defined, in which each membrane can be executed over different cores in a parallel manner. The proposed algorithm can be executed over different cores and uses multi-core processing to implement parallel membrane computation. Simulation with a Colville minimization problem shows that the membrane computing inspired genetic algorithm has improved performance, with a mean error of the solution 61.9 times better than genetic algorithm

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“…To further improve the solution efficiency of RCGAs, many achievements and remarkable efforts have been completed and reported in the past few decades. According to mechanisms and techniques used, the previous developments and attempts made for RCGAs can be classified into the following categories: (1) the determination of an optimal population size [6][7][8][9]; (2) the initialization of population [10][11][12]; (3) the automatic adjustment of operator parameters [13][14][15]; (4) the control of population diversity [16][17][18][19]; (5) the improvement of existing crossover operators [20][21][22]; (5) the development of new crossover schemes [23][24][25][26][27][28][29][30][31][32]; (6) the investigation of novel evolutionary strategies [33][34][35]; and (7) the hybrid use of evolutionary operators [36][37][38][39]. The above summary shows that much emphasis has been placed to improve the crossover operations.…”

Section: Introductionmentioning

confidence: 99%