A decomposition-based multiobjective evolutionary algorithm with angle-based adaptive penalty

Qiao, Junfei; Zhou, Hongbiao; Yang, Cuili; Yang, Shengxiang

doi:10.1016/j.asoc.2018.10.028

Cited by 37 publications

(9 citation statements)

References 34 publications

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“…The penalty factor incremental strategy was proposed to balance the diversity and convergence [45]. After this pioneering work, the dynamic adjustment strategy of penalty factor was paid great attention particularly in [2]. A unique weight vectors adjustment strategy was devised in [46].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Transfer Reference Point-Oriented MOEA/D Involving Local Objective-Space Knowledge

Xie

Yang

Wang

et al. 2022

IEEE Trans. Evol. Computat.

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The decomposition-based multi-objective evolutionary algorithm (MOEA/D) has attained excellent performance in solving optimization problems involving multiple conflicting objectives. However, the Pareto optimal front (POF) of many multi-objective optimization problems (MOPs) has irregular properties, which weakens the performance of MOEA/D. To address this issue, we devise a dynamic transfer reference point oriented MOEA/D with local objective-space knowledge (DTR-MOEA/D). The design principle is based on three original and rigorous mechanisms. First, the individuals are projected onto a line segment (two-objective case) or a three-dimensional plane (three-objective case) after being normalized in the objective space. The line segment or the plane is divided into three different regions: the central region, the middle region, and the edge region. Second, a dynamic transfer criterion of reference point is developed based on population density relationships in different regions. Third, a strategy of population diversity enhancement guided by local objective-space knowledge is adopted to improve the diversity of the population. Finally, the experimental results conducted on sixteen benchmark MOPs and eight modified MOPs with irregular POF shapes verify that the proposed DTR-MOEA/D has attained a strong competitiveness compared with other representative algorithms.

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Section: Related Workmentioning

confidence: 99%

Dynamic Transfer Reference Point-Oriented MOEA/D Involving Local Objective-Space Knowledge

Xie

Yang

Wang

et al. 2022

IEEE Trans. Evol. Computat.

Self Cite

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“…The individual uses the information of adjacent solutions to evolve in order to obtain the optimal solution. At present, the improvement of MOEA/D is mainly concerned with improvements in the weight vector, decomposition method, matching selection, evolutionary operator, replacement and selection method, and computing resource allocation [38][39][40][41][42][43][44]. Various improvement methods have been carried out according to the basic framework of MOEA/D.…”

Section: Basic Frameworkmentioning

confidence: 99%

Using the Decomposition-Based Multi-Objective Evolutionary Algorithm with Adaptive Neighborhood Sizes and Dynamic Constraint Strategies to Retrieve Atmospheric Ducts

Mai

Shi

Liao

et al. 2020

Sensors

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The traditional method of retrieving atmospheric ducts is to use the special sensor of weather balloons or rocket soundings to obtain information intelligently, and it is very expensive. Today, with the development of technology, it is very convenient to retrieve the atmospheric ducts from Global Navigation Satellite System (GNSS) phase delay and propagation loss observation data, and then the GNSS receiver on the ground forms an automatic receiving sensor. This paper proposes a hybrid decomposition-based multi-objective evolutionary algorithm with adaptive neighborhood sizes (EN-MOEA/ACD-NS), which dynamically imposes some constraints on the objectives. The decomposition-based multi-objective evolutionary algorithm (MOEA/D) updates the solutions through neighboring objectives, the number of which affects the quality of the optimal solution. Properly constraining the optimization objectives can effectively balance the diversity and convergence of the population. The experimental results from the Congress on Evolutionary Computation (CEC) 2009 on test instances with hypervolume (HV), inverted generational distance (IGD), and average Hausdorff distance ∆ 2 metrics show that the new method performs similarly to the evolutionary algorithm MOEA/ACD-NS, which considers only the dynamic change of the neighborhood sizes. The improved algorithm is applied to the practical problem of jointly retrieving atmospheric ducts with GNSS signals, and its performance further demonstrates its feasibility and practicability. the multi-objective optimization can make full use of multi-source observation information and can improve the reliability of the solution. The traditional method can only obtain a Pareto optimal solution that is not necessarily satisfactory to the decision-maker; the rationality of prior knowledge also affects the selection of the optimal solution. This paper proposes a multi-objective evolutionary algorithm (MOEA) for MOP problems in order to obtain a set of Pareto optimal solutions [5,6]. According to the optimization framework, MOEAs can be roughly divided into three categories, namely: (a) Pareto-based approaches [7], (b) indicator-based approaches [8], and (c) decomposition-based approaches [9].Pareto-based methods sort the individuals according to the dominant relationship among them, and then the optimal solution is selected. The Pareto-based approach is the most popular method in the evolutionary multi-objective optimization community. NSGA-II and SPEA2 are the most representative algorithms, and they are very effective when dealing with two or three objective functions [7,10]. However, when the number of objective functions increases to more than three, most of the solutions in the NSGA-II and SPEA2 search spaces become non-dominated solutions, resulting in a rapid decline in search capabilities.Indicator-based MOEAs use the value of performance indicators to guide the search direction, which drives whole generated solutions to the Pareto solution sets. As the calculation of the Pareto dominance relatio...

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“…Ob obj− mod = Ob obj + Penalty (16) The specific application of PFA method can be referred to literatures [19]- [21]. Proverbially, the performance of PFA method is closely related to the appropriateness of penalty coefficients.…”

Section: A: Penalty Function Approachmentioning

confidence: 99%

“…a) Compute the relative performance of the So 1 solution in contrast to the So 2 one (P uv (So 1 )) as formula (21).…”

Section: ) Fudf Indexmentioning

confidence: 99%

Multi-Objective Optimal Power Flow Based on Hybrid Firefly-Bat Algorithm and Constraints- Prior Object-Fuzzy Sorting Strategy

et al. 2019

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In this paper, a novel hybrid firefly-bat algorithm with constraints-prior object-fuzzy sorting strategy (HFBA-COFS) is put forward to solve the strictly-constrained multi-objective optimal power flow (MOOPF) problems. The hybrid firefly-bat algorithm (HFBA) integrates the dimension-based firefly algorithm and the modified bat algorithm to improve the population-diversity and global-exploration ability of original algorithm. To handle the unqualified state variables and overcome the deficiency of traditional penalty function approach (PFA), the constraints-prior Pareto-dominant rule (CPR) which takes constraintsviolation and objectives-value into consideration is proposed in this paper. Furthermore, an effective constraints-prior object-fuzzy sorting (COFS) strategy based on CPR rule is presented to seek the welldistributed Pareto optimal set (POS) in solving the MOOPF problems. To validate the great advantages of HFBA-COFS algorithm, ten MOOPF cases optimizing active power loss, total emission and fuel cost are simulated on the IEEE 30-bus, IEEE 57-bus and IEEE 118-bus systems. In addition, the generational distance and SPREAD evaluation indexes powerfully demonstrate that the proposed HFBA-COFS algorithm can achieve high-quality POS, which has great significance to realize the safe and economic operation of large-scale power systems.INDEX TERMS Hybrid firefly-bat algorithm, constraints-prior object-fuzzy sorting strategy, multi-objective optimal power flow problem, economic operation.

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A decomposition-based multiobjective evolutionary algorithm with angle-based adaptive penalty

Cited by 37 publications

References 34 publications

Dynamic Transfer Reference Point-Oriented MOEA/D Involving Local Objective-Space Knowledge

Dynamic Transfer Reference Point-Oriented MOEA/D Involving Local Objective-Space Knowledge

Using the Decomposition-Based Multi-Objective Evolutionary Algorithm with Adaptive Neighborhood Sizes and Dynamic Constraint Strategies to Retrieve Atmospheric Ducts

Multi-Objective Optimal Power Flow Based on Hybrid Firefly-Bat Algorithm and Constraints- Prior Object-Fuzzy Sorting Strategy

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