Dynamic Multi Objective Particle Swarm optimization with Cooperative Agents

Kouka, Najwa; Fdhila, Raja; Hussain, Amir; Alimi, Adel M.

doi:10.1109/cec48606.2020.9285979

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Agents-based Approaches To Optimization1

State-of-the-art Moeas1

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2021

2024

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Cited by 7 publications

(2 citation statements)

References 23 publications

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“…After the evolutionary process of each sub-swarm, agents save the best solutions discovered during the search in the shared archive. This method has been recently adopted to solve dynamic multi-objective problems [13].…”

Section: Agents-based Approaches To Optimizationmentioning

confidence: 99%

A novel approach of many-objective particle swarm optimization with cooperative agents based on an inverted generational distance indicator

Kouka

BenSaid

Fdhila

et al. 2023

Information Sciences

Self Cite

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Section: Agents-based Approaches To Optimizationmentioning

confidence: 99%

A novel approach of many-objective particle swarm optimization with cooperative agents based on an inverted generational distance indicator

Kouka

BenSaid

Fdhila

et al. 2023

Information Sciences

Self Cite

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“…Under this direction, the use of multi-objective particle swarm optimizers (MOPSOs) is a viable alternative. Kouka et al [42] designed a MOPSO to tackle dynamic MOPs where the key contribution is the use of multiple populations and cooperative agents that share knowledge to deal with the changing search environment. Their experimental results showed the effectiveness of this approach.…”

Section: State-of-the-art Moeasmentioning

confidence: 99%

On the Effect of the Cooperation of Indicator-Based Multiobjective Evolutionary Algorithms

Falcón-Cardona

Ishibuchi

Coello

et al. 2021

IEEE Trans. Evol. Computat.

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For almost 20 years, quality indicators (QIs) have promoted the design of new selection mechanisms of multiobjective evolutionary algorithms (MOEAs). Each indicatorbased MOEA (IB-MOEA) has specific search preferences related to its baseline QI, producing Pareto front approximations with different properties. In consequence, an IB-MOEA based on a single QI has a limited scope of multi-objective optimization problems (MOPs) in which it is expected to have a good performance. This issue is emphasized when the associated Pareto front geometries are highly irregular. In order to overcome these issues, we propose here an island-based multi-indicator algorithm (IMIA) that takes advantage of the search biases of multiple IB-MOEAs through a cooperative scheme. Our experimental results show that the cooperation of multiple IB-MOEAs allows IMIA to perform more robustly (considering several QIs) than the panmictic versions of its baseline IB-MOEAs as well as several stateof-the-art MOEAs. Additionally, IMIA shows a Pareto-frontshape invariance property which makes it a remarkable optimizer when tackling MOPs with complex Pareto front geometries.

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A Study on the Performance of Grey Wolf Optimizer

Çiftçioğlu

2021

Communications in Computer and Information Science

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Dynamic Multi Objective Particle Swarm optimization with Cooperative Agents

Cited by 7 publications

References 23 publications

A novel approach of many-objective particle swarm optimization with cooperative agents based on an inverted generational distance indicator

A novel approach of many-objective particle swarm optimization with cooperative agents based on an inverted generational distance indicator

On the Effect of the Cooperation of Indicator-Based Multiobjective Evolutionary Algorithms

A Study on the Performance of Grey Wolf Optimizer

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