A Benchmark-Suite of real-World constrained multi-objective optimization problems and some baseline results

Kumar, Abhishek; Wu, Guohua; Ali, Mostafa Z.; Luo, Qizhang; Mallipeddi, Rammohan; Suganthan, Ponnuthurai Nagaratnam; Das, Swagatam

doi:10.1016/j.swevo.2021.100961

Cited by 117 publications

(19 citation statements)

References 52 publications

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“…The most commonly used performance indicator when optimizing CMOPs is Hypervolume (HV ) [1], [45], which quantifies the volume of the objective space covered by PF and a reference point to measure PF convergence and distribution. The reference point, r, is a vector that has objective values worse than any values in the PF.…”

Section: E Performance Spacementioning

confidence: 99%

“…C ONSTRAINED multi-objective optimization problems (CMOPs) involve searching for the best trade-off between multiple conflicting objectives subject to one or more constraints. Many real-world optimization problems match this description, in areas as diverse as mechanical design, chemical engineering, and power system optimization [1]. Generally, a CMOP is more challenging than its unconstrained counterpart due to the addition of one or more constraint functions, and the resulting interactions between the constraints and the objectives [2].…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, several constrained multi-objective evolutionary algorithms (CMOEAs) have been introduced to specifically tackle CMOPs [3]. However, as per many other problem domains, practice has shown that no single algorithm outperforms all other algorithms across all problem instances [1], [4]. Each algorithm has its strengths and weaknesses, and it is hard to choose the best one for solving a particular instance.…”

Section: Introductionmentioning

confidence: 99%

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An Instance Space Analysis of Constrained Multi-Objective Optimization Problems

Alsouly,

Kirley,

Muñoz

2022

Preprint

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Multi-objective optimization problems with constraints (CMOPs) are generally considered more challenging than those without constraints. This in part can be attributed to the creation of infeasible regions generated by the constraint functions, and/or the interaction between constraints and objectives. In this paper, we explore the relationship between constrained multi-objective evolutionary algorithms (CMOEAs) performance and CMOP instances characteristics using Instance Space Analysis (ISA). To do this, we extend recent work focused on the use of Landscape Analysis features to characterise CMOP. Specifically, we scrutinise the multi-objective landscape and introduce new features to describe the multi-objective-violation landscape, formed by the interaction between constraint violation and multi-objective fitness. Detailed evaluation of problemalgorithm footprints spanning six CMOP benchmark suites and fifteen CMOEAs, illustrates that ISA can effectively capture the strength and weakness of the CMOEAs. We conclude that two key characteristics, the isolation of non-dominate set and the correlation between constraints and objectives evolvability, have the greatest impact on algorithm performance. However, the current benchmarks problems do not provide enough diversity to fully reveal the efficacy of CMOEAs evaluated.

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Section: E Performance Spacementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Instance Space Analysis of Constrained Multi-Objective Optimization Problems

Alsouly,

Kirley,

Muñoz

2022

Preprint

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“…All the mentioned test suites consist of artificially designed CMOPs and are not derived from real-world applications. To overcome this weakness, a novel suite named RCM was proposed in [14]. The RCM suite collects 50 real-world optimization problems based on physical models, including problems from mechanical design, chemical engineering, power electronics, etc.…”

Section: A Test Suitesmentioning

confidence: 99%

“…In contrast to the related work, we also discuss the scalability of the proposed techniques. Finally, the most frequently used artificial test suites are compared against selected real-world problems from the RCM suite-a novel test suite consisting of real-world CMOPs based on physical models [14]. Specifically, we assess whether the studied artificial test problems comprehensively represent the characteristics observed in the RCM problems.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Constrained Continuous Multiobjective Optimization Problems: A Feature Space Perspective

Vodopija,

Tušar,

Filipič

2021

Preprint

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Despite the increasing interest in constrained multiobjective optimization in recent years, constrained multiobjective optimization problems (CMOPs) are still unsatisfactory understood and characterized. For this reason, the selection of appropriate CMOPs for benchmarking is difficult and lacks a formal background. We address this issue by extending landscape analysis to constrained multiobjective optimization. By employing four exploratory landscape analysis techniques, we propose 29 landscape features (of which 19 are novel) to characterize CMOPs. These landscape features are then used to compare eight frequently used artificial test suites against a recently proposed suite consisting of real-world problems based on physical models. The experimental results reveal that the artificial test problems fail to adequately represent some realistic characteristics, such as strong negative correlation between the objectives and constraints. Moreover, our findings show that all the studied artificial test suites have advantages and limitations, and that no "perfect" suite exists. Benchmark designers can use the obtained results to select or generate appropriate CMOP instances based on the characteristics they want to explore.

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Multi‐objective Firefly algorithm for enhanced balanced exploitation and exploration capabilities

Liu

2023

Concurrency and Computation

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SummaryThe multi‐objective Firefly algorithm has a single strategy for finding the best in the evolutionary process, which is easy to fall into the local optimum and leads to poor distribution and convergence of the population. To address this problem, this article proposes an enhanced multi‐objective Firefly algorithm with balanced exploitation and exploration capability (MOFA‐EBE). The convergence evaluation index is introduced to divide the population into two sub‐regions according to the difference of convergence, namely, the development area and exploration area, and each sub‐region is assigned its learning strategy to maximize the utilization of population information. Since the individuals in the development region are far from the Pareto front, the Lévy flights mechanism is added to expand the search area and make them approach the Pareto front quickly under the guidance of the convergent global optimal particles to improve the convergence of the algorithm; since the individuals in the exploration region already have better convergence, they are assigned the most diverse and convergent global individuals for guidance and the Cauchy The variation mechanism is added to the Pareto frontier for continuous exploration to improve the distributivity of the algorithm. In the experimental part, the algorithm is compared with some multi‐objective optimization algorithms on 19 benchmark test functions, and the effectiveness of the added strategy of MOFA‐EBE is verified. The results show that MOFA‐EBE is significantly superior to several other algorithms in terms of improving population convergence and distributivity.

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A Benchmark-Suite of real-World constrained multi-objective optimization problems and some baseline results

Cited by 117 publications

References 52 publications

An Instance Space Analysis of Constrained Multi-Objective Optimization Problems

An Instance Space Analysis of Constrained Multi-Objective Optimization Problems

Characterization of Constrained Continuous Multiobjective Optimization Problems: A Feature Space Perspective

Multi‐objective Firefly algorithm for enhanced balanced exploitation and exploration capabilities

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