This research paper proposes a memetic algorithm based on a hybridization of two metaheuristic approaches, a single-solution method and a compact optimization algorithm. The hybrid algorithm is thus a bi-module framework, where each module encapsulates a different search logic. Both modules use the Non-Uniform Mutation, although with different flavors: the first one acting on a single variable at a time, the second one acting on multiple variables. Hence, the algorithm is dubbed “compact Single/Multi Non-Uniform Mutation” (in short, cSM). It is designed for being suitable for tackling optimization problems on memory-constrained devices, i.e., devices for which the available memory may be not enough to run population-based metaheuristics. The performance of cSM is evaluated by an extensive comparative analysis including 12 state-of-the-art memory-saving (also called “lightweight”) algorithms on three well-known testbeds, namely the BBOB, the CEC-2014, and CEC-2017 benchmarks, as well as seven real-world optimization problems included in the CEC-2011 benchmark. In the case of the CEC benchmarks, our method is also compared against the top (population-based) algorithms that participated in respective competitions. The numerical results indicate that, compared to all the other lightweight algorithms under study, the proposed algorithm is better at handling most functions at different dimensionalities, especially in the case of non-separable problems.

A single-solution–compact hybrid algorithm for continuous optimization / Khalfi, Souheila; Iacca, Giovanni; Draa, Amer. - In: MEMETIC COMPUTING. - ISSN 1865-9284. - 2022:(2022). [10.1007/s12293-022-00386-5]

A single-solution–compact hybrid algorithm for continuous optimization

Iacca, Giovanni
;
2022-01-01

Abstract

This research paper proposes a memetic algorithm based on a hybridization of two metaheuristic approaches, a single-solution method and a compact optimization algorithm. The hybrid algorithm is thus a bi-module framework, where each module encapsulates a different search logic. Both modules use the Non-Uniform Mutation, although with different flavors: the first one acting on a single variable at a time, the second one acting on multiple variables. Hence, the algorithm is dubbed “compact Single/Multi Non-Uniform Mutation” (in short, cSM). It is designed for being suitable for tackling optimization problems on memory-constrained devices, i.e., devices for which the available memory may be not enough to run population-based metaheuristics. The performance of cSM is evaluated by an extensive comparative analysis including 12 state-of-the-art memory-saving (also called “lightweight”) algorithms on three well-known testbeds, namely the BBOB, the CEC-2014, and CEC-2017 benchmarks, as well as seven real-world optimization problems included in the CEC-2011 benchmark. In the case of the CEC benchmarks, our method is also compared against the top (population-based) algorithms that participated in respective competitions. The numerical results indicate that, compared to all the other lightweight algorithms under study, the proposed algorithm is better at handling most functions at different dimensionalities, especially in the case of non-separable problems.
2022
Khalfi, Souheila; Iacca, Giovanni; Draa, Amer
A single-solution–compact hybrid algorithm for continuous optimization / Khalfi, Souheila; Iacca, Giovanni; Draa, Amer. - In: MEMETIC COMPUTING. - ISSN 1865-9284. - 2022:(2022). [10.1007/s12293-022-00386-5]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/361362
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