Investigation of memory-based multi-objective optimization evolutionary algorithm in dynamic environment

Wang, Yu; Li, Bin

doi:10.1109/cec.2009.4983004

Cited by 58 publications

(38 citation statements)

References 16 publications

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“…Due to the sufficient results, our future work aims to decrease the computational time, which is necessary for timely reactions of the system. This solution was able to synthesize a new charge scenario in 2-3 min, which may be decreased using parallel computing or evolutionary optimization enhanced with memory [45]. …”

Section: Discussionmentioning

confidence: 99%

Multi Objective Optimization in Charge Management of Micro Grid Based Multistory Carpark

2018

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Distributed power supply with the use of renewable energy sources and intelligent energy flow management has undoubtedly become one of the pressing trends in modern power engineering, which also inspired researchers from other fields to contribute to the topic. There are several kinds of micro grid platforms, each facing its own challenges and thus making the problem purely multi objective. In this paper, an evolutionary driven algorithm is applied and evaluated on a real platform represented by a private multistory carpark equipped with photovoltaic solar panels and several battery packs. The algorithm works as a core of an adaptive charge management system based on predicted conditions represented by estimated electric load and production in the future hours. The outcome of the paper is a comparison of the optimized and unoptimized charge management on three different battery setups proving that optimization may often outperform a battery setup with larger capacity in several criteria.

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Section: Discussionmentioning

confidence: 99%

Multi Objective Optimization in Charge Management of Micro Grid Based Multistory Carpark

2018

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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: 95%

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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“…Therefore, using the FDA1 DMOOP alone to test whether an algorithm can solve DMOOPs is not sufficient. Mehnen et al 2006;Zeng et al 2006;Bingul 2007;Cámara et al 2007aCámara et al , 2007bZheng 2007;Zhou et al 2007;Greeff and Engelbrecht 2008;Isaacs et al 2008;Tan and Goh 2008;Wang and Dang 2008;Chen et al 2009;Tan 2009b, 2009a;Lechuga 2009;Wang and Li 2009;Cámara et al 2009Cámara Sola 2010;Greeff and Engelbrecht 2010;Koo et al 2010;Wang and Li 2010; Engelbrecht 2011] Modified [Zhou et al 2007] FDA2…”

Section: Dmoo Benchmark Functions Currently Usedmentioning

confidence: 99%

“…Original [Farina et al 2004;Zeng et al 2006;Cámara et al 2007aCámara et al , 2007bLiu and Wang 2007;Wang and Dang 2008;Greeff and Engelbrecht 2010;Liu 2010;Wang and Li 2010; Engelbrecht 2011] Modified [Mehnen et al 2006;Deb et al 2007;Zheng 2007;Isaacs et al 2008;Lechuga 2009;Cámara et al 2009Cámara Sola 2010; [Mehnen et al 2006;Liu and Wang 2007;Tan 2009b, 2009a;Wang and Li 2009;Koo et al 2010;Liu 2010;Wang and Li 2010;Helbig and Engelbrecht 2011] Several researchers have used the FDA2 DMOOP. However, the POF of FDA2 changes from a convex to a concave shape only for specific values of the decision variables [Mehnen et al 2006;Deb et al 2007], as can be seen, for example, in Engelbrecht [2011, 2013b].…”

Section: Dmoo Benchmark Functions Currently Usedmentioning

confidence: 99%

“…In addition, dCOEA struggled to find solutions for FDA5 dec , even in slow-changing environments. DMOOPs with an isolated or deceptive POF are [Zheng 2007;] -Type III: dMOP1 (Equation (13)) -DMOOPs with a nonconvex POF -Type I: DMZDT2 [Wang and Li 2009], FDA4 (Equation (8)), DMOP3 ] -Type II: FDA5 (Equation (9) (19)), HE2 (Equation (20)) -DMOOPs with a nonuniform spread of solutions -Type I: dMOP3 (Equation (15)) -Type II: FDA5 (Equation (9)), modified FDA3 functions [Zheng 2007;] -Type III: Modified FDA5 3. DMOOPs with various types or shapes of POSs -Types I, II: DTLZ Av (Equation (21)) -Type II: ZJZ (Equation (16)), DSW2 (Equation (28)), DSW3 (Equation (29)) -Type III: HE3 to HE10 (Equations (34) through (41)) -Types II, III: Modified FDA2 [Mehnen et al 2006;Deb et al 2007;Zheng 2007;Isaacs et al 2008;Lechuga 2009;Cámara et al 2009Cámara Sola 2010] 4.…”

Section: Evaluation Of Dmoo Algorithmsmentioning

confidence: 99%

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Benchmarks for dynamic multi-objective optimisation algorithms

Helbig

Engelbrecht

2014

ACM Comput. Surv.

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Algorithms that solve Dynamic Multi-Objective Optimisation Problems (DMOOPs) should be tested on benchmark functions to determine whether the algorithm can overcome specific difficulties that can occur in real-world problems. However, for Dynamic Multi-Objective Optimisation (DMOO), no standard benchmark functions are used. A number of DMOOPs have been proposed in recent years. However, no comprehensive overview of DMOOPs exist in the literature. Therefore, choosing which benchmark functions to use is not a trivial task. This article seeks to address this gap in the DMOO literature by providing a comprehensive overview of proposed DMOOPs, and proposing characteristics that an ideal DMOO benchmark function suite should exhibit. In addition, DMOOPs are proposed for each characteristic. Shortcomings of current DMOOPs that do not address certain characteristics of an ideal benchmark suite are highlighted. These identified shortcomings are addressed by proposing new DMOO benchmark functions with complicated Pareto-Optimal Sets (POSs), and approaches to develop DMOOPs with either an isolated or deceptive Pareto-Optimal Front (POF). In addition, DMOO application areas and real-world DMOOPs are discussed.

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Investigation of memory-based multi-objective optimization evolutionary algorithm in dynamic environment

Cited by 58 publications

References 16 publications

Multi Objective Optimization in Charge Management of Micro Grid Based Multistory Carpark

Multi Objective Optimization in Charge Management of Micro Grid Based Multistory Carpark

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

Benchmarks for dynamic multi-objective optimisation algorithms

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