A Multiple Reference Point-based evolutionary algorithm for dynamic multi-objective optimization with undetectable changes

Azzouz, Radhia; Bechikh, Slim; Saïd, Lamjed Ben

doi:10.1109/cec.2014.6900569

Cited by 31 publications

(21 citation statements)

References 16 publications

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“…Also, many other approaches have been proposed such as memorybased approaches (Ramsey and Grefenstette 1993;Yang and Yao 2008;Peng et al 2011), multipopulation approaches (Oppacher and Wineberg 1999;Li and Yang 2012;Ursem 2000), predictive approaches (Bosman 2007;Zhang et al 2008). Contrary to the single-objective case, there are few works dealing with DMOPs which include change prediction approaches (Zhou et al 2007(Zhou et al , 2014Hatzakis and Wallace 2006;Koo et al 2010;Li et al 2014), memory-based approaches (Goh et al 2009;Wang and Li 2009), parallel approaches (Cámara et al 2007), and other approaches (Deb 2011;Huang et al 2011;Amato and Farina 2005;Azzouz et al 2014).…”

Section: Introductionmentioning

confidence: 92%

“…2. Thus, we propose to use in a first step, a population management strategy based on our local search technique, proposed in Azzouz et al (2014), that we call Local Search-based Strategy (LS-Strategy). The used local search technique uses an epsilon indicator-based selection method.…”

Section: Ls-strategymentioning

confidence: 99%

“…The proposed membrane control strategy has been applied to the optimal control of a time-varying unstable plant that has been presented as a DMOP. In 2014, Azzouz et al (2014) have proposed the multiple reference point-based multi-objective EA (MRP-MOEA) that deals with dynamic problems with undetectable changes. This algorithm does not need to detect changes.…”

Section: Other Approachesmentioning

confidence: 99%

“…Each set of solutions is used with a specific goal. The local search-based population management strategy is based on our local search technique that we have recently proposed in Azzouz et al (2014). The role of the local search is to find new directions in the search space with the aim to drive the population towards the new interesting regions once a change occurred.…”

Section: Introductionmentioning

confidence: 99%

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A dynamic multi-objective evolutionary algorithm using a change severity-based adaptive population management strategy

2015

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In addition to the need for simultaneously optimizing several competing objectives, many real-world problems are also dynamic in nature. These problems are called dynamic multi-objective optimization problems. Applying evolutionary algorithms to solve dynamic optimization problems has obtained great attention among many researchers. However, most of works are restricted to the single-objective case. In this work, we propose an adaptive hybrid population management strategy using memory, local search and random strategies, to effectively handle environment dynamicity for the multi-objective case where objective functions change over time. Moreover, the proposed strategy is based on a new technique that detects the change severity, according to which it adjusts the number of memory and random solutions to be used. This ensures, on the one hand, a high level of convergence and on the other hand, the required diversity. We propose a dynamic version of the Non dominated Sorting Genetic Algorithm II, within which we integrate the above-mentioned strategies. Empirical results show that our proposal based on the use of the adaptive strategy is able to handle dynamic environments and to track the Pareto front as it changes over time. Moreover, when confronted with several recently proposed dynamic algorithms, it has presented competitive and better results on most problems.Keywords Change severity sensing · Dynamic multiobjective optimization · Time-changing objective functions · Adaptive population management · Memory-based strategy · Local search-based strategy · Change severity-based strategy

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Section: Introductionmentioning

confidence: 92%

Section: Ls-strategymentioning

confidence: 99%

Section: Other Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A dynamic multi-objective evolutionary algorithm using a change severity-based adaptive population management strategy

2015

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“…In DMOPs, the objective functions are not only determined by the problem variables but also changing during the optimization process (time or environment). The main difficulty of DMOPs lies on the fact that the optimal Pareto set with the optimal Pareto front is changing every subperiod of the optimization process …”

Section: Introductionmentioning

confidence: 99%

A novel PSO algorithm for dynamic wireless sensor network multiobjective optimization problem

El-Shorbagy

Elhoseny

Hassanien

et al. 2018

Trans Emerging Tel Tech

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All real‐life multiobjective optimization problems (MOPs) are considered dynamic. It is occurring due to fluctuations in environmental or the global market instabilities that lead to the quick fluctuations of prices. Wireless sensor network optimization problem, by nature, is considered one of the dynamic MOP (DMOP) and needs to be solved by a special method to save time and effort. Thus, this paper proposed a novel particle swarm optimization (PSO) for dynamic wireless sensor network MOP to accelerate data transfer in networks and reduce energy losses. Generally, in DMOPs, the optimization period is broken into several equal subperiods. In each subperiod, there is a dynamic parameter that changes. In the proposed approach, PSO is used to handle DMOPs without any changing of its structure and has fast convergence properties. However, a new mechanism to choose the personal and global preferred particles is introduced, which based on the distance between the nondominated solutions obtained so far and the particles positions by using Euclidean metric. In addition, two types of archives are used to maintain the nondominated solutions. The first one is used to store the nondominated solutions obtained by each particle, whereas the second type is used to store the nondominated solutions achieved by all particles. The novel methodology performance is proved by applying it on three benchmark problems that were chosen from the literature and one design problem from the engineering domain. The simulation results mentioned that the proposed algorithm is active and efficacious in solving DMOPs.

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A Hybrid Multiobjective Optimization Approach for Dynamic Problems: Evolutionary Algorithm Using Hypervolume Indicator

Ouada

Boudali

Tagina

2019

Hybrid Intelligent Systems

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A Multiple Reference Point-based evolutionary algorithm for dynamic multi-objective optimization with undetectable changes

Cited by 31 publications

References 16 publications

A dynamic multi-objective evolutionary algorithm using a change severity-based adaptive population management strategy

A dynamic multi-objective evolutionary algorithm using a change severity-based adaptive population management strategy

A novel PSO algorithm for dynamic wireless sensor network multiobjective optimization problem

A Hybrid Multiobjective Optimization Approach for Dynamic Problems: Evolutionary Algorithm Using Hypervolume Indicator

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