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2018•Unpublished venueRequires access

Large-Scale Evolutionary Optimization Using Multi-Layer Differential Evolution

Tarik Eltaeib, Ausif Mahmood

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Abstract

In this paper, we propose Multi-layer Differential Evolution (MLDE) algorithm that is capable of optimizing large scale problems. A grouping of different strategies are assembled together to solve the problems instead of depend on one strategy. Different strategies applied on individuals to strengthen the ability of exploration of the algorithm. Extensive computational studies are also carried out to evaluate the performance of newly proposed algorithm on a large number of benchmark functions with up to 100 dimensions. A set of well-known CEC 2013 benchmark functions have been used to assessment and evaluate the performance of proposed algorithm.

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What this paper is about

In this paper, we propose Multi-layer Differential Evolution (MLDE) algorithm that is capable of optimizing large scale problems. A grouping of different strategies are assembled together to solve the problems instead of depend on one strategy. Different strategies applied on individuals to strengthen the ability of exploration of the algorithm. Extensive computational studies are also carried out to evaluate the performance of newly proposed algorithm on a large number of benchmark functions with up to 100 dimensions. A set of well-known CEC 2013 benchmark functions have been used to assessment and evaluate the performance of proposed algorithm.

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Available abstract

In this paper, we propose Multi-layer Differential Evolution (MLDE) algorithm that is capable of optimizing large scale problems. A grouping of different strategies are assembled together to solve the problems instead of depend on one strategy. Different strategies applied on individuals to strengthen the ability of exploration of the algorithm. Extensive computational studies are also carried out to evaluate the performance of newly proposed algorithm on a large number of benchmark functions with up to 100 dimensions. A set of well-known CEC 2013 benchmark functions have been used to assessment and evaluate the performance of proposed algorithm.

Key concepts: Benchmark (surveying), Differential evolution, Computer science, Evolutionary algorithm, Evolutionary computation, Set (abstract data type), Scale (ratio), Layer (electronics)

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