Engineering applications of multi-objective evolutionary algorithms: A test suite of box-constrained real-world problems

Zapotecas-Martínez, Saúl; García-Nájera, Abel; Menchaca-Méndez, Adriana

doi:10.1016/j.engappai.2023.106192

Cited by 17 publications

(6 citation statements)

References 55 publications

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“…In this part, the effectiveness of NLDEA was further verified in the practical problem of unconstrained and irregular PF, that is, water and oil repellent fabric development (WORFD) (Ahmad et al, 2017;Zapotecas-Martínez et al, 2023). The textile industry aims to create high-value fabrics, often achieving hydrophobicity, or water and oil repellency, through various process parameters (Sun et al, 2005).…”

Section: Application To Real-world Problemsmentioning

confidence: 99%

A Many-Objective Evolutionary Algorithm Based on Non-Linear Dominance

Zhou

Dai

Xue

2023

International Journal of Swarm Intelligence Research

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With the increase in the number of objectives, the number of non-dominated solutions will also increase sharply. The sorting method based on the traditional Pareto dominance is not sufficiently distinguishable from the solutions and cannot provide enough selection pressure when the population size is small. In this article, a new non-linear dominance (NLD) method is proposed. The main motivation of this method is from the perspective of storage solutions. The number of solutions is small and the difference between each component is as large as possible, so the part of the first quadrant, the second, and the fourth quadrant near the first quadrant becomes the dominant interval, except for the distance too far also defined as the dominant interval, for which construct a parabolic shape of the non-dominant interval. Based on this relationship, the authors propose a non-linear dominated many-objective evolutionary algorithm (NLDEA), which can solve the irregular Pareto front. Experiments show that NLDEA is competitive with the most advanced methods for various scalable benchmark problems.

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Section: Application To Real-world Problemsmentioning

confidence: 99%

A Many-Objective Evolutionary Algorithm Based on Non-Linear Dominance

Zhou

Dai

Xue

2023

International Journal of Swarm Intelligence Research

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“…In order to address the challenges posed by artificial test problems, several real-world test suites [1], [34] have been proposed in the literature, predominantly comprising singleand multi-objective problems. Nevertheless, there has been a lack of literature addressing the development of a comprehensive test suite designed explicitly for many-objective optimization real-world problems.…”

Section: A Motivationsmentioning

confidence: 99%

“…These objectives are: minimizing the distance VOLUME 10, 2022 from the inlet combustion (D cc ), the maximum temperature at injector surface (T Smax ), maximum temperature at the injector post tip (T Pmax ), and temperature at three inches from the injector surface (T is ). In addition to these four objectives, the liquid-rocket singleelement injector design problem involves three decision variables that describe crucial design parameters: the angle at which hydrogen flows, the proportional increase in the hydrogen area relative to the standard cross-sectional area of the tube carrying hydrogen, and the proportional decrease in the oxygen area concerning the standard cross-sectional area of the tube carrying oxygen, as described in [1]. Therefore, the liquid-rocket single-element injector design problem is formulated as…”

Section: Real-world Many-objective Test Suitementioning

confidence: 99%

“…The three decision variables in this context pertain to the concentration of water and oil-repellent agents, crosslinking agents, and the curing temperature, as outlined in [1]. Therefore, the water and oil-repellent fabric development problem is formulated as…”

Section: )mentioning

confidence: 99%

“…Many engineering problems can be formulated as optimization problems involving a specified objective or cost function that has to be minimized or maximized. Solving an optimization problem entails identifying problem parameters, organizing these parameters, and selecting a subset of parameters to obtain the most optimal solution [1]. In practical applications, most engineering optimization problems pose significant challenges due to the intrinsic complexity of determining the best solution within a tolerable computational timeframe.…”

Section: Introductionmentioning

confidence: 99%

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Benchmarking Real-World Many-Objective Problems: A Problem Suite With Baseline Results

Palakonda,

Kang,

Jung

2024

IEEE Access

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In recent decades, multi-objective evolutionary algorithms (MOEAs) have been evaluated on artificial test problems with unrealistic characteristics, leading to uncertain conclusions about their efficacy in real-world applications. To address this issue, a few benchmark test suites comprising real-world problems have been proposed for MOEAs, encompassing numerous multi-objective problems and a select few many-objective problems. Given the distinct challenges posed by many-objective optimization problems (MaOPs) and their inherent difficulty, it is crucial to develop a test suite that includes real-world problems with many conflicting objectives. Hence, in this paper, we propose a comprehensive test suite for benchmarking the many-objective real-world complex problems. This test suite consists of 11 problems collected from different disciplines of engineering. Furthermore, we comprehensively analyzed the problems in our newly proposed test suite, employing eight state-of-the-art algorithms rooted in various fundamental principles specifically designed to address MaOPs. The experimental findings highlight the strong performance of indicator-based, weightvector-based decomposition, Pareto-dominance-based, and hybrid MOEAs on the proposed test suite. In contrast, reference-vector-based decomposition approaches, Pareto front shape estimation-based methods, and multi-evolution approaches exhibit relatively weaker performance.

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Multi-objective optimization of virtual machine migration among cloud data centers

Maldonado Carrascosa,

Seddiki,

Jiménez Sánchez

et al. 2024

Soft Comput

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Workload migration among cloud data centers is currently an evolving task that requires substantial advancements. The incorporation of fuzzy systems holds potential for enhancing performance and efficiency within cloud computing. This study addresses a multi-objective problem wherein the goal is to maximize the interpretability and the percentage of renewable energy consumed by a fuzzy meta-scheduler system in cloud scenarios. To accomplish this objective, the present research proposes a novel approach utilizing a multi-objective Knowledge Acquisition with a Swarm Intelligence Approach algorithm. Additionally, it takes advantage of a framework built on CloudSim, which includes virtual machine migration capabilities based on an expert system. Furthermore, a hierarchical fuzzy system is employed to assess rule base interpretability, along with another multi-objective algorithm, named Non-dominated Sorting Genetic Algorithm II. The framework and hierarchical system are employed to perform various simulation results concerning renewable energy and interpretability, while the algorithms aim to enhance the system’s performance and interpretability. Empirical results demonstrate that it is possible to improve the performance of cloud data centers while improving the interpretability of the corresponding fuzzy rule-based system. The proposed multi-objective algorithm shows comparable or superior performance to the genetic algorithm across diverse scenarios. The simulation results indicate that improvements in cloud data center performance can be achieved while enhancing system interpretability. The average improvement in the interpretability index ranges from 0.6 to 6%, with a corresponding increase in renewable energy utilization ranging from 5 to 6%.

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Engineering applications of multi-objective evolutionary algorithms: A test suite of box-constrained real-world problems

Cited by 17 publications

References 55 publications

A Many-Objective Evolutionary Algorithm Based on Non-Linear Dominance

A Many-Objective Evolutionary Algorithm Based on Non-Linear Dominance

Benchmarking Real-World Many-Objective Problems: A Problem Suite With Baseline Results

Multi-objective optimization of virtual machine migration among cloud data centers

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