A self-adaptive binary differential evolution algorithm for large scale binary optimization problems

Banitalebi, Akbar; Aziz, Mohd Ismail Abd; Aziz, Zainal Abdul

doi:10.1016/j.ins.2016.05.037

Cited by 61 publications

(23 citation statements)

References 42 publications

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“…In mutation stage, several DE trial vector generation strategies have been proposed. Well-known mutation strategies were listed in [21,22]. One of the DE mutation strategies in the literature defined as follows:…”

Section: Differential Evolutionmentioning

confidence: 99%

A Modified Artificial Algae Algorithm For Large Scale Global Optimization Problems

Koçer¹,

Uymaz²

2018

ijisae

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Optimization technology is used to accelerate decision-making processes and to increase the quality of decision making in management and engineering problems. The development technology has made real world problems large and complex. Many optimization methods that proposed for solving large-scale global optimization (LSGO) problems suffer from the "curse of dimensionality", which implies that their performance deteriorates quickly as the dimensionality of the search space increases. Therefore, more efficient and robust algorithms are needed. When literature on large-scale optimization problems is examined, it is seen that algorithms with effective global search ability have better results. For the purpose, in this paper Modified Artificial Algae Algorithm (MAAA) is proposed by modifying original version of Artificial Algae Algorithm (AAA) inspiring by Differential Evolution Algorithm (DE)'s mutation strategies. AAA and MAAA are compared with each other by operating with the first 10 benchmark functions of CEC2010 Special Session on Large Scale Global Optimization. The results show that hybridization process that applied by updating an additional fourth dimension with mutation strategies of DE after the helical motion of the AAA algorithm, contributes exploration phase and improves the AAA performance on LSGO.

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Section: Differential Evolutionmentioning

confidence: 99%

A Modified Artificial Algae Algorithm For Large Scale Global Optimization Problems

Koçer¹,

Uymaz²

2018

ijisae

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“…Note that, apart from the algorithms used in this study, the results of solving CEC2015 test suit obtained from efficient binary artificial bee colony algorithm based on genetic operator (GBABC), binary quantum-inspired gravitational search algorithm (BQIGSA), and self-adaptive binary variant of a differential evolution algorithm (SabDE) as reported in [53] are also included in the comparison. From Table 2, the mean (Mean) and standard deviation (STD) values of the objective functions are used to measure the search convergence and consistency of the algorithms.…”

Section: Cec2015mentioning

confidence: 99%

“…For 9, the best performers are UMDA, BPSO, GA, PBIL, and EDACE, which obtain the same mean values while, for 13, the best performers are UMDA, BPSO, GA, PBIL, BSA, and EDACE, which obtained the same mean values. It should be noted that the results from [53] were obtained from using the total number of function evaluations as 1,000,000 with the binary length of 50 for each design variable whereas this work uses 10,000 function evaluations with the binary length of 5 for each design variable. This indirect comparison with GBABC, BQIGSA, and SabDE can only be used to show that the proposed EDACE also has good performance and cannot be used to claim which method is superior.…”

Section: Cec2015mentioning

confidence: 99%

“…Apart from GA, other MHs using a binary string representing a design solution include a univariate marginal distribution algorithm (UMDA) [48], population-based incremental learning (PBIL) [24], binary particle swarm optimisation (BPSO) [49], binary simulated annealing (BSA) [50], binary artificial bee colony algorithm based on genetic operator (GBABC) [51], binary quantuminspired gravitational search algorithm (BQIGSA) [52], and self-adaptive binary variant of a differential evolution algorithm (SabDE) [53]. With the popularity of GA, a binarycode MH has been rarely developed and proposed while its real-code counterparts have over a hundred different search concepts reported in the literature.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Distribution Algorithm Using Correlation between Binary Elements: A New Binary-Code Metaheuristic

Pholdee

Bureerat

2017

Mathematical Problems in Engineering

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A new metaheuristic called estimation of distribution algorithm using correlation between binary elements (EDACE) is proposed. The method searches for optima using a binary string to represent a design solution. A matrix for correlation between binary elements of a design solution is used to represent a binary population. Optimisation search is achieved by iteratively updating such a matrix. The performance assessment is conducted by comparing the new algorithm with existing binary-code metaheuristics including a genetic algorithm, a univariate marginal distribution algorithm, population-based incremental learning, binary particle swarm optimisation, and binary simulated annealing by using the test problems of CEC2015 competition and one real-world application which is an optimal flight control problem. The comparative results show that the new algorithm is competitive with other established binary-code metaheuristics.

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“…However, most metaheuristics were designed for a continuous search space. A binary search space poses the problem of discontinuity and non-differentiability, which makes it difficult to use classical deterministic optimization methods [9]. In binary problems, it is required to reduce the number of possible states to binary solutions.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Feature Selection Based on Swallow Swarm Optimization for Fuzzy Classification

et al. 2019

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This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric structure of a membership function. Searching for the (sub) optimal subset of features is an NP-hard problem. In this paper, a binary swallow swarm optimization (BSSO) algorithm for feature selection is proposed. To solve the classification problem, we use a fuzzy rule-based classifier. To evaluate the feature selection performance of our method, BSSO is compared to induction without feature selection and some similar algorithms on well-known benchmark datasets. Experimental results show the promising behavior of the proposed method in the optimal selection of features.

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A self-adaptive binary differential evolution algorithm for large scale binary optimization problems

Cited by 61 publications

References 42 publications

A Modified Artificial Algae Algorithm For Large Scale Global Optimization Problems

A Modified Artificial Algae Algorithm For Large Scale Global Optimization Problems

Estimation of Distribution Algorithm Using Correlation between Binary Elements: A New Binary-Code Metaheuristic

Feature Selection Based on Swallow Swarm Optimization for Fuzzy Classification

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