Decomposition-based evolutionary algorithm for large scale constrained problems

Sayed, Eman; Essam, Daryl; Sarker, Ruhul A.; Elsayed, Saber M.

doi:10.1016/j.ins.2014.10.035

Cited by 31 publications

(16 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Accurate decomposition has been shown to have a significant impact on reducing the number of constraint violations [197]. Fitness difference minimization [198] and the differential grouping family [115] are two of the most widely used decomposition methods in large-scale constrained optimization.…”

Section: Constraint Handlingmentioning

confidence: 99%

“…Sayed et al [198] used a fitness difference minimization approach to analyze the interaction structure of the objective and constraint functions and decompose them into a set of smaller subproblems. Aguilar-Justo and Mezura-Montes [199] improved upon [198] and used an aggregate function of all constraint violations, rather than the individual constraints, for interaction analysis and problem decomposition. A problem of fitness difference minimization methods is the need for specifying the number or the size of components.…”

Section: Constraint Handlingmentioning

confidence: 99%

“…Scalable benchmark functions for constrained optimization problems are very limited. Beside the constrained problems compiled by Dolan et al [219], which are not widely used in the EC community, Sayed et al [198,215] also proposed a set of artificial benchmark functions for constrained problems. Recent studies also attempted to extend standard dynamic optimization benchmarks to study large-scale dynamic optimization problems [143,220] Applications: Benchmarks are used to ultimately help with designing and evaluating efficient algorithms for solving realworld problems.…”

Section: E Benchmarks and Applicationsmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Population-Based Metaheuristics for Large-Scale Black-Box Global Optimization—Part II

Omidvar

Yao

2022

IEEE Trans. Evol. Computat.

View full text Add to dashboard Cite

This paper is the second part of a two-part survey series on large-scale global optimization. The first part covered two major algorithmic approaches to large-scale optimization, namely decomposition methods and hybridization methods such as memetic algorithms and local search. In this part we focus on sampling and variation operators, approximation and surrogate modeling, initialization methods, and parallelization. We also cover a range of problem areas in relation to large-scale global optimization, such as multi-objective optimization, constraint handling, overlapping components, the component imbalance issue, and benchmarks, and applications. The paper also includes a discussion on pitfalls and challenges of current research and identifies several potential areas of future research.

show abstract

Section: Constraint Handlingmentioning

confidence: 99%

Section: Constraint Handlingmentioning

confidence: 99%

Section: E Benchmarks and Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Population-Based Metaheuristics for Large-Scale Black-Box Global Optimization—Part II

Omidvar

Yao

2022

IEEE Trans. Evol. Computat.

View full text Add to dashboard Cite

show abstract

“…Cooperative Co-evolution (CC) [9] has been used with some success when 'scaling up' EAs to tackle very high dimensional search and optimization problems. For example, CC has been applied to large scale continuous [10], combinatorial [11], constrained [12], multi-objective [13] and dynamic [14] optimization problems. The CC framework divides the LSGO problem into a number of sub-components, and uses an (several) EA(s) to solve each sub-component cooperatively.…”

Section: Introductionmentioning

confidence: 99%

A Recursive Decomposition Method for Large Scale Continuous Optimization

Sun

Kirley

Halgamuge

2018

IEEE Trans. Evol. Computat.

171

112

View full text Add to dashboard Cite

Cooperative Co-evolution (CC) is an evolutionary computation framework that can be used to solve high dimensional optimization problems via a 'divide-and-conquer' mechanism. However, the main challenge when using this framework lies in problem decomposition. That is, deciding how to allocate decision variables to a particular sub-problem, especially interacting decision variables. Existing decomposition methods are typically computationally expensive. In this paper, we propose a new decomposition method, which we call Recursive Differential Grouping (RDG), by considering the interaction between decision variables based on non-linearity detection. RDG recursively examines the interaction between a selected decision variable and the remaining variables, placing all interacting decision variables into the same sub-problem. We use analytical methods to show that RDG can be used to efficiently decompose a problem, without explicitly examining all pairwise variable interactions. We evaluated the efficacy of the RDG method using large scale benchmark optimization problems. Numerical simulation experiments showed that RDG greatly improved the efficiency of problem decomposition in terms of time complexity. Significantly, when RDG was embedded in a CC framework, the optimization results were better than results from seven other decomposition methods.

show abstract

“…The cooperative co-evolution (CC) [15] framework has been applied with some success in scaling up evolutionary algorithms to solve high-dimensional (large-scale) optimization problems [10,12,16]. It divides a large-scale optimization problem into a number of lowdimensional components that are solved cooperatively.…”

Section: Introductionmentioning

confidence: 99%