Self-adaptive collective intelligence-based mutation operator for differential evolution algorithms

Feng, Jinhong; Zhang, Jundong; Wang, Chuan; Xu, Minyi

doi:10.1007/s11227-019-03044-9

Cited by 5 publications

(3 citation statements)

References 28 publications

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“…Thus, the proposed CI mutation operator may guide the donor vector towards potential better searching areas compared with DE/rand/1 and other mutation strategies. In this paper, the whole self-adaption of m could be described as follows [63]: (1) generate the probability p i of exponentially distributed random number whose mean is µ for each target vector i; (2) generate m i for ith target vector according to p i by using roulette wheel selection; (3) if a better trial vector is obtained, the m i value would be added into the successful record which is named as S m ; (4) estimate the mean value µ of the successful record S m ; (5) go to step (1). The process of CI-based mutation strategy on a 2D parameter space is shown Fig.…”

Section: B Evolutionary Operations Of Sacide-rmentioning

confidence: 99%

Multi-Objective Economic Scheduling of a Shipboard Microgrid Based on Self-Adaptive Collective Intelligence DE Algorithm

et al. 2020

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With the growing demand for emission reductions and fuel efficiency improvements, alternative energy sources and energy storage technologies are becoming popular in a ship microgrid. In order to balance the two non-compatible objectives, a new differential evolution variant, which is named as SaCIDE-r, was proposed to solve the optimization problem. In this algorithm, a Collective Intelligence (CI) based mutation operator was proposed by mixing some promising donor vectors in the current population. Besides, a self-adaptive mechanism which was developed to avoid introducing extra control parameters. Further, to avoid being trapped in local optima, a re-initialization mechanism was developed. Then, we have evaluated the performances of the proposed SaCIDE-r approach by studying some numerical optimization problems of Congress on Evolutionary Computation (CEC) 2013 with D = 30, compared with seven stateof-the-art DE algorithms. Moreover, the proposed SaCIDE-r method was applied for economic scheduling of a shipboard microgrid under different cases compared with other multi-objective optimizing methods, resulting in very competitive performances. The comprehensive experimental results have demonstrated that the presented SaCIDE-r method might be a feasible solution for such a kind of optimization problem. INDEX TERMS Shipboard microgrid, global optimization, collective intelligence (CI), differential evolution (DE).

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Section: B Evolutionary Operations Of Sacide-rmentioning

confidence: 99%

Multi-Objective Economic Scheduling of a Shipboard Microgrid Based on Self-Adaptive Collective Intelligence DE Algorithm

et al. 2020

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“…In [37], m is just designed as a random integer within [1,NP], resulting in a randomly generated integer which obeys exponential distribution. In another work [22], m was developed as an exponential-distributed random number self-adapted alongside evolution. In order to improve the performance of the proposed mutation operator, we would like to present a new self-learning adjusting method for m. Initially, at each iteration, m is randomized within [1,NP] and rounded as an integer for each donor vector.…”

Section: A Mutationmentioning

confidence: 99%

“…Moreover, DE and similar algorithms have been used to optimize ANNs [8], [9], [21]. However, the performances of DE algorithms need more improvements [22], [23].…”

Section: Introductionmentioning

confidence: 99%

A Collective Intelligence Based Differential Evolution Algorithm for Optimizing the Structure and Parameters of a Neural Network

et al. 2020

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In this paper, a Self-learning Collective Intelligence Differential Evolution (SLCIDE) algorithm was proposed to optimize both the architecture and parameters of a Feedforward Neural Network (FNN). In order to improve the exploration-exploitation capability, a new Collective Intelligence (CI) based mutation operator was proposed by mixing some promising donor vectors in the current population. Besides, a self-learning mechanism which was designed to adaptively select m top ranked donor vectors was developed by using a widely used unsupervised learning method, k-means. As a result, the proposed approach can be more adaptive and statistically powerful on versatile problems. Then, we evaluated the performances of the proposed SLCIDE approach by studying some numerical optimization problems of CEC 2013 with D = 30 and D = 50. Further, the proposed SLCIDE method was applied to train a FNN on four most popular datasets, resulting in very competitive performances. The comprehensive experimental results have demonstrated that the presented SLCIDE method obtain better results compared with other state-of-the-art algorithms. INDEX TERMS Evolutionary artificial neural network, global optimization, collective intelligence (CI), differential evolution (DE).

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A supercomputing method for large-scale optimization: a feedback biogeography-based optimization with steepest descent method

Zhang

Gao²,

Guo³

2022

J Supercomput

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Self-adaptive collective intelligence-based mutation operator for differential evolution algorithms

Cited by 5 publications

References 28 publications

Multi-Objective Economic Scheduling of a Shipboard Microgrid Based on Self-Adaptive Collective Intelligence DE Algorithm

Multi-Objective Economic Scheduling of a Shipboard Microgrid Based on Self-Adaptive Collective Intelligence DE Algorithm

A Collective Intelligence Based Differential Evolution Algorithm for Optimizing the Structure and Parameters of a Neural Network

A supercomputing method for large-scale optimization: a feedback biogeography-based optimization with steepest descent method

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