Results 201 to 210 of about 50,463 (272)
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Journal of the ACM, 1991
Summary: A multiobjective generalization of the heuristic search algorithm \(A^*\) is presented, called \(MOA^*\). The research is motivated by the observation that most real-world problems involve multiple, conflicting, and noncommensurate objectives.
Chelsea C White
exaly +2 more sources
Summary: A multiobjective generalization of the heuristic search algorithm \(A^*\) is presented, called \(MOA^*\). The research is motivated by the observation that most real-world problems involve multiple, conflicting, and noncommensurate objectives.
Chelsea C White
exaly +2 more sources
Hierarchy Ranking Method for Multimodal Multiobjective Optimization With Local Pareto Fronts
IEEE Transactions on Evolutionary Computation, 2023Multimodal multiobjective problems (MMOPs) commonly arise in real-world situations where distant solutions in decision space share a very similar objective value.
Wenhua Li +4 more
semanticscholar +1 more source
Multiobjective Evolution Strategy for Dynamic Multiobjective Optimization
IEEE Transactions on Evolutionary Computation, 2020This article presents a novel evolution strategy-based evolutionary algorithm, named DMOES, which can efficiently and effectively solve multiobjective optimization problems in dynamic environments. First, an efficient self-adaptive precision controllable mutation operator is designed for individuals to explore and exploit the decision space.
Kai Zhang 0002 +3 more
openaire +1 more source
Nonlinear Dynamics, Psychology, and Life Sciences, 2003
Multiobjective oligopoly models are constructed. The objectives of the first two models are to maximize profits and to maximize sales. In the third model the objectives are to maximize profits and to minimize risk. Giving more weight to risk minimization decreased the profits.
E, Ahmed, A S, Hegazi, A T Abd, El-Hafez
openaire +2 more sources
Multiobjective oligopoly models are constructed. The objectives of the first two models are to maximize profits and to maximize sales. In the third model the objectives are to maximize profits and to minimize risk. Giving more weight to risk minimization decreased the profits.
E, Ahmed, A S, Hegazi, A T Abd, El-Hafez
openaire +2 more sources
Multiobjective Lévy-Flight Firefly Algorithm for Multiobjective Optimization
2021The firefly algorithm (FA) was firstly proposed during 2008–2009 as one of the powerful population-based metaheuristic optimization techniques for solving continuous and combinatorial optimization problems. The FA has been proved and applied to various real-world problems in mostly single objective optimization manner. However, many real-world problems
Somchai Sumpunsri +2 more
openaire +1 more source
Multiobjective multifactorial immune algorithm for multiobjective multitask optimization problems
Applied Soft Computing Journal, 2021Abstract Inspired by human brains’ ability to solve multiple tasks simultaneously, evolutionary multitasking is proposed to improve the overall efficiency of optimizing multiple tasks simultaneously by reusing the learned knowledge. The immune algorithm is inspired by the biological immune system that has been proven to be effective in many practical
Zhiwei Xu, Kai Zhang
exaly +2 more sources
Adaptive Offspring Generation for Evolutionary Large-Scale Multiobjective Optimization
IEEE Transactions on Systems, Man & Cybernetics. Systems, 2022Offspring generation plays an important role in evolutionary multiobjective optimization. However, generating promising candidate solutions effectively in high-dimensional spaces is particularly challenging.
Cheng He, Ran Cheng, D. Yazdani
semanticscholar +1 more source
IEEE Transactions on Cybernetics, 2022
Constrained multiobjective optimization problems (CMOPs) involve multiple objectives to be optimized and various constraints to be satisfied, which challenges the evolutionary algorithms in balancing the objectives and constraints.
Jing J. Liang +6 more
semanticscholar +1 more source
Constrained multiobjective optimization problems (CMOPs) involve multiple objectives to be optimized and various constraints to be satisfied, which challenges the evolutionary algorithms in balancing the objectives and constraints.
Jing J. Liang +6 more
semanticscholar +1 more source
Multiobjective Patient Stratification Using Evolutionary Multiobjective Optimization
IEEE Journal of Biomedical and Health Informatics, 2018One of the main challenges in modern medic-ine is to stratify patients for personalized care. Many different clustering methods have been proposed to solve the problem in both quantitative and biologically meaningful manners. However, existing clustering algorithms suffer from numerous restrictions such as experimental noises, high dimensionality, and ...
Xiangtao Li, Ka-Chun Wong
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A Survey on the Hypervolume Indicator in Evolutionary Multiobjective Optimization
IEEE Transactions on Evolutionary Computation, 2021Hypervolume is widely used as a performance indicator in the field of evolutionary multiobjective optimization (EMO). It is used not only for performance evaluation of EMO algorithms (EMOAs) but also in indicator-based EMOAs to guide the search.
Ke Shang +3 more
semanticscholar +1 more source