Insights into the effects of control parameters and mutation strategy on self-adaptive ensemble-based differential evolution

Wang, Shir Li; Morsidi, Farid; Ng, Theam Foo; Budiman, Haldi; Neoh, Siew Chin

doi:10.1016/j.ins.2019.11.046

Cited by 18 publications

(7 citation statements)

References 36 publications

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“…Therefore, various research efforts for its improvement are continuously carried out even though the algorithm has been introduced 25 years ago by Storn and Price [10]. Besides, surveys related DE variants are updated after a certain years to accommodate various modifications of DEs, as shown in [5,9,[11][12][13][14][15]. The surveys show that most of the research efforts focus on improving the performance of DE through parameter settings and modifications in genetic operations consisting of initialization, differential mutation, crossover and selection.…”

Section: Improved Differential Evolutionmentioning

confidence: 99%

“…The surveys show that most of the research efforts focus on improving the performance of DE through parameter settings and modifications in genetic operations consisting of initialization, differential mutation, crossover and selection. Another potential direction towards improving DE is the use of hybridization, and it has gained research attention [9,11,12].…”

Section: Improved Differential Evolutionmentioning

confidence: 99%

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A Hybrid of Fully Informed Particle Swarm and Self-Adaptive Differential Evolution for Global Optimization

et al. 2022

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Evolutionary computation algorithms (EC) and swarm intelligence have been widely used to solve global optimization problems. The optimal solution for an optimization problem is called by different terms in EC and swarm intelligence. It is called individual in EC and particle in swarm intelligence. Self-adaptive differential evolution (SaDE) is one of the promising variants of EC for solving global optimization problems. Adapting self-manipulating parameter values into SaDE can overcome the burden of choosing suitable parameter values to create the next best generation’s individuals to achieve optimal convergence. In this paper, a fully informed particle swarm (FIPS) is hybridized with SaDE to enhance SaDE’s exploitation capability while maintaining its exploration power so that it is not trapped in stagnation. The proposed hybrid is called FIPSaDE. FIPS, a variant of particle swarm optimization (PSO), aims to help solutions jump out of stagnation by gathering knowledge about its neighborhood’s solutions. Each solution in the FIPS swarm is influenced by a group of solutions in its neighborhood, rather than by the best position it has visited. Indirectly, FIPS increases the diversity of the swarm. The proposed algorithm is tested on benchmark test functions from “CEC 2005 Special Session on Real-Parameter Optimization” with various properties. Experimental results show that the FIPSaDE is more effective and reasonably competent than its standalone variants, FIPS and SaDE, in solving the test functions, considering the solutions’ quality.

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Section: Improved Differential Evolutionmentioning

confidence: 99%

Section: Improved Differential Evolutionmentioning

confidence: 99%

A Hybrid of Fully Informed Particle Swarm and Self-Adaptive Differential Evolution for Global Optimization

et al. 2022

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“…Numerous variants of DE have been developed to improve the reliability, scalability, accuracy, convergence speed and other aspects of the method. Parameter adaptation methods were introduced that resulted in self-adaptive DE variants, such as SaDE [41] and ensemble-based SAEDE [42]. The mutation rule was improved by using the best member from the selected fraction of individuals and an external archive containing suboptimal individuals JADE [43].…”

Section: Related Workmentioning

confidence: 99%

Solution of Mixed-Integer Optimization Problems in Bioinformatics with Differential Evolution Method

et al. 2021

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The solution of the so-called mixed-integer optimization problem is an important challenge for modern life sciences. A wide range of methods has been developed for its solution, including metaheuristics approaches. Here, a modification is proposed of the differential evolution entirely parallel (DEEP) method introduced recently that was successfully applied to mixed-integer optimization problems. The triangulation recombination rule was implemented and the recombination coefficients were included in the evolution process in order to increase the robustness of the optimization. The deduplication step included in the procedure ensures the uniqueness of individual integer-valued parameters in the solution vectors. The developed algorithms were implemented in the DEEP software package and applied to three bioinformatic problems. The application of the method to the optimization of predictors set in the genomic selection model in wheat resulted in dimensionality reduction such that the phenotype can be predicted with acceptable accuracy using a selected subset of SNP markers. The method was also successfully used to optimize the training set of samples for such a genomic selection model. According to the obtained results, the developed algorithm was capable of constructing a non-linear phenomenological regression model of gene expression in developing a Drosophila eye with almost the same average accuracy but significantly less standard deviation than the linear models obtained earlier.

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“…DE is a new evolutionary population-based algorithm that has been typically utilized in numerical optimization [66]. In DE, each individual (solution) of the population competes with its parents, and the fittest wins [67].…”

Section: Differential Evolution Algorithm In Rbfnn-2satramentioning

confidence: 99%

Artificial Immune System in Doing 2-Satisfiability Based Reverse Analysis Method via a Radial Basis Function Neural Network

Alzaeemi

Sathasivam

2020

Processes

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A radial basis function neural network-based 2-satisfiability reverse analysis (RBFNN-2SATRA) primarily depends on adequately obtaining the linear optimal output weights, alongside the lowest iteration error. This study aims to investigate the effectiveness, as well as the capability of the artificial immune system (AIS) algorithm in RBFNN-2SATRA. Moreover, it aims to improve the output linearity to obtain the optimal output weights. In this paper, the artificial immune system (AIS) algorithm will be introduced and implemented to enhance the effectiveness of the connection weights throughout the RBFNN-2SATRA training. To prove that the introduced method functions efficiently, five well-established datasets were solved. Moreover, the use of AIS for the RBFNN-2SATRA training is compared with the genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and artificial bee colony (ABC) algorithms. In terms of measurements and accuracy, the simulation results showed that the proposed method outperformed in the terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Schwarz Bayesian Criterion (SBC), and Central Process Unit time (CPU time). The introduced method outperformed the existing four algorithms in the aspect of robustness, accuracy, and sensitivity throughout the simulation process. Therefore, it has been proven that the proposed AIS algorithm effectively conformed to the RBFNN-2SATRA in relation to (or in terms of) the average value of training of RMSE rose up to 97.5%, SBC rose up to 99.9%, and CPU time by 99.8%. Moreover, the average value of testing in MAE was rose up to 78.5%, MAPE—rose up to 71.4%, and was capable of classifying a higher percentage (81.6%) of the test samples compared with the results for the GA, DE, PSO, and ABC algorithms.

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Insights into the effects of control parameters and mutation strategy on self-adaptive ensemble-based differential evolution

Cited by 18 publications

References 36 publications

A Hybrid of Fully Informed Particle Swarm and Self-Adaptive Differential Evolution for Global Optimization

A Hybrid of Fully Informed Particle Swarm and Self-Adaptive Differential Evolution for Global Optimization

Solution of Mixed-Integer Optimization Problems in Bioinformatics with Differential Evolution Method

Artificial Immune System in Doing 2-Satisfiability Based Reverse Analysis Method via a Radial Basis Function Neural Network

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