Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems

Awad, Noor H.; Ali, Mostafa Z.; Suganthan, Ponnuthurai Nagaratnam

doi:10.1109/cec.2017.7969336

Cited by 255 publications

(105 citation statements)

References 17 publications

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“…3. LSHADE-cnEpSin (LSCNE) [82]: New version of LSHADE-EpSin that uses an ensemble of sinusoidal approaches based on the current adaptation and a modification of the crossover operator with a covariance matrix.…”

Section: Jsomentioning

confidence: 99%

Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review

Carrasco¹,

García²,

Rueda³

et al. 2020

Swarm and Evolutionary Computation

402

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A key aspect of the design of evolutionary and swarm intelligence algorithms is studying their performance. Statistical comparisons are also a crucial part which allows for reliable conclusions to be drawn. In the present paper we gather and examine the approaches taken from different perspectives to summarise the assumptions made by these statistical tests, the conclusions reached and the steps followed to perform them correctly. In this paper, we conduct a survey on the current trends of the proposals of statistical analyses for the comparison of algorithms of computational intelligence and include a description of the statistical background of these tests. We illustrate the use of the most common tests in the context of the Competition on single-objective real parameter optimisation of the IEEE Congress on Evolutionary Computation (CEC) 2017 and describe the main advantages and drawbacks of the use of each kind of test and put forward some recommendations concerning their use.According to Pesarin [63], P is a non-parametric family of distributions if it is not possible to find a finite-dimensional space Θ in which there is a one-to-one relationship between Θ and P. This means that we do not have to assume that the underlying distribution belongs to a known family of distributions. Consequently, the prerequisites for non-parametric tests such as symmetry or 9

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Section: Jsomentioning

confidence: 99%

Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review

Carrasco¹,

García²,

Rueda³

et al. 2020

Swarm and Evolutionary Computation

402

View full text Add to dashboard Cite

show abstract

“…This algorithm was further improved by the same author and they introduced jSO [26] that used a new weighted version of mutation operator. To maintain an effective balance between exploitation and exploration, Awad et al [27] proposed a new way to adapt LSHADE control parameters (i.e., F and Cr). This is done by using a new ensemble sinusoidal mechanism that automatically tunes F and Cr values.…”

Section: A De and Its Variantsmentioning

confidence: 99%

Evolutionary Framework With Reinforcement Learning-Based Mutation Adaptation

et al. 2020

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Although several multi-operator and multi-method approaches for solving optimization problems have been proposed, their performances are not consistent for a wide range of optimization problems. Also, the task of ensuring the appropriate selection of algorithms and operators may be inefficient since their designs are undertaken mainly through trial and error. This research proposes an improved optimization framework that uses the benefits of multiple algorithms, namely, a multi-operator differential evolution algorithm and a co-variance matrix adaptation evolution strategy. In the former, reinforcement learning is used to automatically choose the best differential evolution operator. To judge the performance of the proposed framework, three benchmark sets of bound-constrained optimization problems (73 problems) with 10, 30 and 50 dimensions are solved. Further, the proposed algorithm has been tested by solving optimization problems with 100 dimensions taken from CEC2014 and CEC2017 benchmark problems. A real-world application data set has also been solved. Several experiments are designed to analyze the effects of different components of the proposed framework, with the best variant compared with a number of stateof-the-art algorithms. The experimental results show that the proposed algorithm is able to outperform all the others considered.

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“…Here, we analyzed the performance of BIMA on 30 benchmark functions of CEC2017 [45] with the recently developed and most popular population-based optimization techniques, namely DA [19], GWO [13], PSO [3], DE [27], SSA [18], LSHADE [46], L-SHADE SPACMA [47] L-SHADE-cnEpSin [48] and CMA-ES [49]. Performance is evaluated on the basis of 50 search agents and the dimension of the search agent was set to 30 and simulated the evaluation for 30 independent runs having 500 iterations each.…”

Section: • Fixed Dimension Multi-modal Function (F14-f23)mentioning

confidence: 99%

Buyer Inspired Meta-Heuristic Optimization Algorithm

Debnath

Arif

Baishya

2020

Open Computer Science

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Nature inspired swarm based meta-heuristic optimization technique is getting considerable attention and established to be very competitive with evolution based and physical based algorithms. This paper proposes a novel Buyer Inspired Meta-heuristic optimization Algorithm (BIMA) inspired form the social behaviour of human being in searching and bargaining for products. In BIMA, exploration and exploitation are achieved through shop to shop hoping and bargaining for products to be purchased based on cost, quality of the product, choice and distance to the shop. Comprehensive simulations are performed on 23 standard mathematical and CEC2017 benchmark functions and 3 engineering problems. An exhaustive comparative analysis with other algorithms is done by performing 30 independent runs and comparing the mean, standard deviation as well as by performing statistical test. The results showed significant improvement in terms of optimum value, convergence speed, and is also statistically more significant in comparison to most of the reported popular algorithms.

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Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems

Cited by 255 publications

References 17 publications

Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review

Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review

Evolutionary Framework With Reinforcement Learning-Based Mutation Adaptation

Buyer Inspired Meta-Heuristic Optimization Algorithm

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