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Review of the Research Landscape of Multi-Criteria Evaluation and Benchmarking Processes for many-objective optimization Methods: Coherent Taxonomy, Challenges and Recommended Solution

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2020年第6期19卷 1619-1693页

作者： Mohammed, R. T. Yaakob, R. Zaidan, A. A. Sharef, N. M. Abdullah, R. H. Zaidan, B. B. Dawood, K. A. Univ Pendidikan Sultan Idris UPSI Comp Dept Tanjung Malim Malaysia Univ Putra Malaysia UPM Fac Comp Sci & Informat Technol Seri Kembangan Selangor Malaysia

Evaluation and benchmarking of many-objective optimization (MaOO) methods are complicated. The rapid development of new optimization algorithms for solving problems with many objectives has increased the necessity of developing performance indicators or metrics for evaluating the performance quality and comparing the competing optimization algorithms fairly. Further investigations are required to highlight the limitations of how criteria/metrics are determined and the consistency of the procedures with the evaluation and benchmarking processes of MaOO. A review is conducted in this study to map the research landscape of multi-criteria evaluation and benchmarking processes for MaOO into a coherent taxonomy. Then contentious and challenging issues related to evaluation are highlighted, and the performance of optimization algorithms for MaOO is benchmarked. The methodological aspects of the evaluation and selection of MaOO algorithms are presented as the recommended solution on the basis of four distinct and successive phases. First, in the determination phase, the evaluation criteria of MaOO are collected, classified and grouped for testing experts' consensus on the most suitable criteria. Second, the identification phase involves the process of establishing a decision matrix via a crossover of the 'evaluation criteria' and MaOO', and the level of importance of each selective criteria and sub-criteria from phase one is computed to identify its weight value by using the best-worst method (BWM). Third, the development phase involves the creation of a decision matrix for MaOO selection on the basis of the integrated BWM and VIKOR method. Last, the validation phase involves the validation of the proposed solution.

关键词： Evaluation fuzzy Delphi many-objective optimization many-objective optimization problem multi-criteria decision making (MCDM)

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UAVs routes optimization on smart cities and regions

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RAIRO-OPERATIONS RESEARCH 2022年第2期56卷 853-869页

作者： Marques, Elias L., Jr. Coelho, Vitor N. Coelho, Igor M. Frota, Yuri A. de M. Koochaksaraei, Roozbeh H. Ochi, Luiz Satoru Coelho, Bruno N. Univ Fed Fluminense Inst Comp Av Gal Milton Tavares de Souza BR-24210310 Niteroi RJ Brazil OptBlocks Consultoria Ltda Ave Joao Pinheiro274 Sala 201 BR-30130186 Belo Horizonte MG Brazil George Washington Univ Comp Sci Dept Adjunct Fac Washington DC 20052 USA Univ Fed Ouro Preto Dept Control & Automat Engn Campus Morro do Cruzeiro BR-35400000 Ouro Preto MG Brazil

Unmanned Aerial Vehicles are becoming a common technology used on smart cities and smart regions, thus requiring optimization of its routes with crucial importance. In this innovative work, six objective functions are optimized in order to provide sets of non-dominated solutions, composed of routes with different characteristics. Realistic constraints are considered such as obstacles and areas in which drones could not pass through. A didactic case of study considering points of a graph is used in order to illustrate a smart city composed of different regions. Obtained solutions are analyzed using a state-of-the-art visualization tool, which guides the comprehension of harmony and conflicts between objectives.

关键词： Smart City UAVs routing problem MILP many-objective optimization problem

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Comparison of many-objective evolutionary algorithms using performance metrics ensemble

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ADVANCES IN ENGINEERING SOFTWARE 2014年 76卷 1-8页

作者： He, Z. Yen, G. G. Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

In this study, we have thoroughly researched on performance of six state-of-the-art Multiobjective Evolutionary Algorithms (MOEAs) under a number of carefully crafted many-objective optimization benchmark problems. Each MOEA apply different method to handle the difficulty of increasing objectives. Performance metrics ensemble exploits a number of performance metrics using double elimination tournament selection and provides a comprehensive measure revealing insights pertaining to specific problem characteristics that each MOEA could perform the best. Experimental results give detailed information for performance of each MOEA to solve many-objective optimization problems. More importantly, it shows that this performance depends on two distinct aspects: the ability of MOEA to address the specific characteristics of the problem and the ability of MOEA to handle high-dimensional objective space. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Performance metrics ensemble many-objective optimization problem Evolutionary algorithms Double elimination tournament selection many-objective evolutionary algorithms Performance metrics

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many-objective approach based on problem-aware mutation operators for protein encoding

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INFORMATION SCIENCES 2022年 613卷 376-400页

作者： Diaz-Galian, Maria Victoria Vega-Rodriguez, Miguel A. Univ Extremadura Escuela Politecn Campus Univ S-N Caceres 10003 Spain

One of the research pathways in synthetic biology is protein encoding, which aims to improve and increase protein expression. The production of heterologous proteins is important in different fields, such as medicine, environmental biology, and agriculture. Protein encoding is a difficult task and can be defined as a many-objective optimization problem, where four objectives must be optimized concurrently: codon adaptation, guanine-cytosine content, difference between sequences, and avoidance of hairpin loops. These objectives are why we propose and describe a many-objective approach based on the non-dominated sorting genetic algorithm-III (NSGA-III) for protein encoding. The proposed algorithm was designed to work with novel mutation operators that are problem-aware and consider the different optimization objectives. The results of the pro-posed algorithm were compared to those of different tools that were reported by other authors to determine its efficiency. Comparisons were made using several quality metrics and with statistical analyses. The average improvements in hypervolume and set coverage were between 5.34% and 55.75%, and 53.01% and 100%, respectively. Based on the statisti-cal analyses, the proposed algorithm was found to produce statistically significant improvements compared to other algorithms, thus highlighting the relevance of the proposed approach for protein encoding.(c) 2022 Elsevier Inc. All rights reserved.

关键词： many-objective optimization problem Non-dominated Sorting Genetic Algorithm-III problem-aware mutations Bioinformatics Protein encoding

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Including preferences into a multiobjective evolutionary algorithm to deal with many-objective engineering optimization problems

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INFORMATION SCIENCES 2014年 277卷 1-20页

作者： Lopez-Jaimes, Antonio Coello Coello, Carlos A. CINVESTAV IPN Dept Comp Sci Mexico City 14000 DF Mexico

In this paper, we introduce a new preference relation based on a reference point approach. This relation offers an easy approach to integrate decision maker's preferences into a MOEA without modifying its basic structure. Besides finding the optimal solution of the achievement scalarizing function, the new preference relation allows the decision maker to find a set of solutions around that optimal solution. Then, a MOEA equipped with the proposed preference relation can be integrated into an interactive optimization method. One of the main advantages of the new method is that setting its parameters is an intuitive task to the decision maker. The other advantage is that, since our preference relation induces a finer order on vectors of objective space than that achieved by the Pareto dominance relation, it is appropriate to cope with problems having a high number of objectives. We evaluated the proposed preference relation an engineering problem, the optimization of an airfoil design with 6 objectives. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Evolutionary computation Multi-objective optimization many-objective optimization problem Interactive optimization method

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many-objective Deployment optimization for a Drone-Assisted Camera Network

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2021年第4期8卷 2756-2764页

作者： Cao, Bin Li, Meng Liu, Xin Zhao, Jianwei Cao, Wenxi Lv, Zhihan Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300130 Peoples R China Hebei Univ Technol Sch Artificial Intelligence Tianjin 300401 Peoples R China Hebei Univ Technol Sch Econ & Management Tianjin 300401 Peoples R China Chinese Acad Sci South China Sea Inst Oceanol Guangdong Key Lab Ocean Remote Sensing Guangzhou 510301 Peoples R China Qingdao Univ Sch Data Sci & Software Engn Qingdao 266071 Peoples R China

Drone-assisted camera networks can be used in many applications. However, different application requirements lead to different deployment scenarios. In this paper, based on a 3D terrain environment represented by triangular mesh data, a many-objective optimization model for the deployment of multiple onboard cameras is constructed. We propose an improved version of the constrained two-archive evolutionary algorithm. A selection operator based on Gaussian process regression is used for enhancement. Additionally, we quantize the polynomial mutation operator. The improved algorithm is applied to optimize drone-assisted camera deployment, and the experimental results show that the improved algorithm is superior to state-of-the-art algorithms.

关键词： Cameras Three-dimensional displays optimization Solid modeling Visualization Drones Gaussian processes Drone-assisted camera network Gaussian process regression many-objective optimization problem quantization of polynomial mutation operator

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A many-objective evolutionary algorithm based on vector angle distance scaling

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第5期40卷 10285-10306页

作者： Li, Xin Li, Xiaoli Wang, Kang Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Minist Educ Beijing Key Lab Computat Intelligence & Intellige Engn Res Ctr Digital Community Beijing Peoples R China

In the past two decades, multi-objective evolutionary algorithms (MOEAs) have achieved great success in solving two or three multi-objective optimization problems. As pointed out in some recent studies, however, MOEAs face many difficulties when dealing with many-objective optimization problems(MaOPs) on account of the loss of the selection pressure of the non-dominant candidate solutions toward the Pareto front and the ineffective design of the diversity maintenance mechanism. This paper proposes a many-objective evolutionary algorithm based on vector guidance. In this algorithm, the value of vector angle distance scaling(VADS) is applied to balance convergence and diversity in environmental selection. In addition, tournament selection based on the aggregate fitness value of VADS is applied to generate a high quality offspring population. Besides, we adopt an adaptive strategy to adjust the reference vector dynamically according to the scales of the objective functions. Finally, the performance of the proposed algorithm is compared with five state-of-the-art many-objective evolutionary algorithms on 52 instances of 13 MaOPs with diverse characteristics. Experimental results show that the proposed algorithm performs competitively when dealing many-objective with different types of Pareto front.

关键词： Vector angle distance scaling evolutionary algorithm many-objective optimization problem

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Extension of the Directed Search Domain algorithm for multi-objective optimization to higher dimensions

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ANNALS OF OPERATIONS RESEARCH 2024年第2-3期340卷 1125-1160页

作者： Yu, Boxi Utyuzhnikov, Sergey Univ Manchester Dept Fluids & Environm Manchester M13 9PL England

This paper addresses the problem of generating an evenly distributed set of Pareto solutions. It appears in real-life applications related to multi-objective optimization when it is important to represent the entire Pareto front with a minimal cost. There exist only a few algorithms which are able to tackle this problem in a general formulation. The Directed Search Domain (DSD) algorithm has proved to be efficient and quite universal. It has successfully been applied to different challengeable test cases. In this paper for the first time the DSD approach is systematically extended and applied to problems with higher dimensions. The modified algorithm does not have any formal limitation on the number of objective functions that is important for practical applications. The efficacy of the algorithm is demonstrated on a number of test cases.

关键词： Multi-objective optimization Directed Search Domain Pareto set Evenly distributed set many-objective optimization problem

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Solving the Food-Energy-Water Nexus problem via Intelligent optimization Algorithms 20

Solving the Food-Energy-Water Nexus Problem via Intelligent ...

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IEEE 20th International Conference on Automation Science and Engineering (CASE)

作者： Deng, Qi Fan, Zheng Li, Zhi Pan, Xinna Kang, Qi Zhou, MengChu Tongji Univ Dept Control Sci & Engn Shanghai 201804 Peoples R China Google Dept Trust & Safety YouTube Mountain View CA 94043 USA Zhejiang Gongshang Univ Sch Informat & Elect Engn Hangzhou 310018 Peoples R China

ISBN: (纸本)9798350358513;9798350358520

The application of evolutionary algorithms (EAs) to multi-objective optimization problems has been widespread. However, the EA research community has not paid much attention to large-scale multi-objective optimization problems arising from real-world applications. Especially, Food-EnergyWater systems are intricately linked among food, energy and water that impact each other. They usually involve a huge number of decision variables and many conflicting objectives to be optimized. Solving their related optimization problems is essentially important to sustain the high-quality life of human beings. Their solution space size expands exponentially with the number of decision variables. Searching in such a vast space is challenging because of such large numbers of decision variables and objective functions. In recent years, a number of large-scale many-objectives optimization evolutionary algorithms have been proposed. In this paper, we solve a Food-Energy-Water optimization problem by using the state-of-art intelligent optimization methods and compare their performance. Our results conclude that the algorithm based on an inverse model outperforms the others. This work should be highly instrumental for practitioners to select the most suitable method for their particular large-scale engineering optimization problems.

关键词： Evolutionary algorithms Food-Energy-Water system many-objective optimization problem Intelligent optimization Inverse model Multi-objective optimization

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An Improved Visualization Approach in many-objective optimization

An Improved Visualization Approach in Many-Objective Optimiz...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： He, Zhenan Yen, Gary G. Sichuan Univ Coll Comp Sci Chengdu Sichuan Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

ISBN: (纸本)9781509006229

In a high-dimensional objective space, visualization of population in an approximate Pareto front is crucial to decision making process. By directly observing the performance of each solution, the trade-off between objectives, and distribution of approximate front, the decision maker can effectively decide which solution should be chosen from. Furthermore, visualization throughout the evolution process can also be exploited in designing effective many-objective evolutionary algorithms. Recently, a new visualization approach was developed by constructing a mapping from a high dimensional objective space into a two dimensional polar coordinate system, where a group of predefined direction vectors divide the whole space into a number of sub regions and each individual is associated with one weight vector. This method can be scalable to any dimensions, and simultaneously deal with a large number of individuals and multiple Pareto fronts for the purpose of visual comparison. It faithfully preserves shape, location, range, and distribution of Pareto front. However, distributions of Pareto front within each subregion and relations between different sub regions cannot be observed by this method. In this paper, in order to overcome this deficiency, we incorporate a modified multi-dimensional scaling (MDS) approach into this method. Experimental results show that the modified MDS is a suitable complementary to the existing method. Furthermore, the new design combined with existing visualization approach provides a comprehensive mean to visualize all important information in a many-objective optimization problem.

关键词： visualization many-objective optimization problem multi-dimensional scaling

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