An adaptive coevolutionary Differential Evolution algorithm for large-scale optimization

Differential evolution algorithm (DE) is one of the most applied meta-heuristics algorithm for solving global optimization problems. However, the contributions of applying DE for large-scale global optimization problems are still limited compared with those problems for low dimensions. In this chapter, a new differential evolution algorithm is proposed in order to solve large-scale optimization problems. The proposed algorithm is called differential evolution with space partitioning (DESP). In DESP algorithm, the search variables are divided into small groups of partitions. Each partition contains a certain number of variables and this partition is manipulated as a subspace in the search process. Searching a limited number of variables in each partition prevents the DESP algorithm from wandering in the search space especially in large-scale spaces. The proposed algorithm is investigated on 15 benchmark functions and compared against three variants DE algorithms. The results show that the proposed algorithm is a cheap algorithm and obtains good results in a reasonable time.

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Quantum differential evolution with cooperative coevolution framework and hybrid mutation strategy for large scale optimization

Knowledge-Based Systems ◽

10.1016/j.knosys.2021.107080 ◽

2021 ◽

Vol 224 ◽

pp. 107080

Author(s):

Wu Deng ◽

Shifan Shang ◽

Xing Cai ◽

Huimin Zhao ◽

Yongquan Zhou ◽

...

Keyword(s):

Differential Evolution ◽

Large Scale ◽

Large Scale Optimization ◽

Cooperative Coevolution ◽

Mutation Strategy ◽

Scale Optimization

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Research on Differential Evolution Algorithm Based on Interaction Balance Method for Large-Scale Industrial Processes of Fuzzy Model

Artificial Intelligence and Computational Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-642-16530-6_21 ◽

2010 ◽

pp. 169-176

Author(s):

He Dakuo ◽

Zhao Yuanyuan ◽

Wang Lifeng ◽

Chang Hongrui

Keyword(s):

Differential Evolution ◽

Large Scale ◽

Differential Evolution Algorithm ◽

Fuzzy Model ◽

Balance Method ◽

Industrial Processes ◽

Evolution Algorithm ◽

Interaction Balance

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Phylogenetic Differential Evolution

Natural Computing for Simulation and Knowledge Discovery ◽

10.4018/978-1-4666-4253-9.ch002 ◽

2014 ◽

pp. 22-40 ◽

Cited By ~ 1

Author(s):

Vinícius Veloso de Melo ◽

Danilo Vasconcellos Vargas ◽

Marcio Kassouf Crocomo

Keyword(s):

Differential Evolution ◽

Large Scale ◽

Differential Evolution Algorithm ◽

Building Blocks ◽

Large Scale Systems ◽

Estimation Of Distribution ◽

Evolution Algorithm ◽

A New Technique ◽

Distribution Algorithms ◽

Binary Problems

This paper presents a new technique for optimizing binary problems with building blocks. The authors have developed a different approach to existing Estimation of Distribution Algorithms (EDAs). Our technique, called Phylogenetic Differential Evolution (PhyDE), combines the Phylogenetic Algorithm and the Differential Evolution Algorithm. The first one is employed to identify the building blocks and to generate metavariables. The second one is used to find the best instance of each metavariable. In contrast to existing EDAs that identify the related variables at each iteration, the presented technique finds the related variables only once at the beginning of the algorithm, and not through the generations. This paper shows that the proposed technique is more efficient than the well known EDA called Extended Compact Genetic Algorithm (ECGA), especially for large-scale systems which are commonly found in real world problems.

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