A New Multiagent Algorithm for Dynamic Continuous Optimization

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Author(s): Julien Lepagnot (Université de Paris 12, France), Amir Nakib (Université de Paris 12, France), Hamouche Oulhadj (Université de Paris 12, France)and Patrick Siarry (Université de Paris 12, France)
Copyright: 2012
Pages: 23
Source title: Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends
Source Author(s)/Editor(s): Peng-Yeng Yin (Ming Chuan University, Taiwan)
DOI: 10.4018/978-1-4666-0270-0.ch009

Keywords: Algorithms / Artificial Intelligence / Computer Science & IT / Information Science Reference

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Abstract

Many real-world problems are dynamic and require an optimization algorithm that is able to continuously track a changing optimum over time. In this paper, a new multiagent algorithm is proposed to solve dynamic problems. This algorithm is based on multiple trajectory searches and saving the optima found to use them when a change is detected in the environment. The proposed algorithm is analyzed using the Moving Peaks Benchmark, and its performances are compared to competing dynamic optimization algorithms on several instances of this benchmark. The obtained results show the efficiency of the proposed algorithm, even in multimodal environments.

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A New Multiagent Algorithm for Dynamic Continuous Optimization

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