Multi-objectiveoptimizationproblems (MOPs) are commonly encountered in the study and design of complex systems. Pareto dominance is the most common relationship used to compare solutions in MOPs, however as the numb...
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Multi-objectiveoptimizationproblems (MOPs) are commonly encountered in the study and design of complex systems. Pareto dominance is the most common relationship used to compare solutions in MOPs, however as the number of objectives grows beyond three, Pareto dominance alone is no longer satisfactory. These problems are termed "many-objective optimization problems (MaOPs)". While most MaOP algorithms are modifications of common MOP algorithms, determining the impact on their computational complexity is difficult. This paper defines computational complexity measures for these algorithms and applies these measures to a Multi-objective Evolutionary Algorithm (MOEA) and its MaOP counterpart. (C) 2014 The Authors. Published by Elsevier B.V.
Multi-objectiveoptimizationproblems (MOPs) are commonly encountered in the study and design of complex systems. Pareto dominance is the most common relationship used to compare solutions in MOPs, however as the numb...
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Multi-objectiveoptimizationproblems (MOPs) are commonly encountered in the study and design of complex systems. Pareto dominance is the most common relationship used to compare solutions in MOPs, however as the number of objectives grows beyond three, Pareto dominance alone is no longer satisfactory. These problems are termed “many-objective optimization problems (MaOPs)”. While most MaOP algorithms are modifications of common MOP algorithms, determining the impact on their computational complexity is difficult. This paper defines computational complexity measures for these algorithms and applies these measures to a Multi-objective Evolutionary Algorithm (MOEA) and its MaOP counterpart.
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-objectiveoptimization benchmark problems. Ea...
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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-objectiveoptimization 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.
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 st...
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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.
Advanced mobile communication and data processing technologies have promoted the development of Internet of Things (IoT), but they have also posed challenges to the distributed federated learning mode in the field of ...
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Advanced mobile communication and data processing technologies have promoted the development of Internet of Things (IoT), but they have also posed challenges to the distributed federated learning mode in the field of Internet of Vehicles (IoV). Faced with a large number of vehicle nodes available for federated training in IoV, the federated learning training task becomes challenging when motivating a large number of vehicles participant. A difficulty posed for federated learning in IoV is heterogeneity challenges caused by massive device participation. Moreover, excessive resource and system maintenance costs associated with a large number of poor-quality devices participating in federated training cannot be ignored. To address these issues, this paper proposes a novel vehicle device selection and aggregation joint optimization model based on a many-objective evolutionary algorithm. The proposed model can be optimized by BiGE algorithm to obtain an optimal subset of vehicle equipment and corresponding weight assignment scheme, thus reducing unnecessary resources waste and budget expenditure while ensuring global model performance. To verify the feasibility of the model, several sets of experiments are conducted to demonstrate that our proposed model has acceptable performance while largely reducing the number budget of participants.
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