A Hybrid DE-ACO Algorithm for the Global Optimization

Hybrid metaheuristic algorithms have become popular for solving complex problems that are very challenging to overcome with conventional methods in recent years. This paper presents a hybrid algorithm called DE-ACO that combines Differential Evolution (DE) and Ant Colony Optimization (ACO) algorithm...

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Bibliographic Details
Published in2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS) pp. 1 - 6
Main Authors Abi, Soufiane, Benhala, Bachir, Bouyghf, Hamid
Format Conference Proceeding
LanguageEnglish
Published IEEE 02.12.2020
Subjects
Ant colony optimization
Benchmark testing
Convergence
Differential Evolution
Flowcharts
Hybrid-Metaheuristics
Linear programming
Optimization
Signal processing algorithms
Test Functions Benchmark
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DOI10.1109/ICECOCS50124.2020.9314533

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Summary:Hybrid metaheuristic algorithms have become popular for solving complex problems that are very challenging to overcome with conventional methods in recent years. This paper presents a hybrid algorithm called DE-ACO that combines Differential Evolution (DE) and Ant Colony Optimization (ACO) algorithms. The main idea is to use the Differential Evolution (DE) algorithm for a good starting point for Ant Colony Optimization (ACO) algorithm. A benchmark of six well-known test functions is employed to check the performances of the proposed approach in terms of convergence rate, quality of optimum solutions and computing time. The results obtained show that the performances of the hybrid algorithm outperform significantly the DE and ACO algorithms.
DOI:10.1109/ICECOCS50124.2020.9314533