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Splitting for Multi-objective Optimization
Methodology and Computing in Applied Probability, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Qibin Duan, Dirk P. Kroese
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2011 IEEE Conference on Computational Intelligence and Games (CIG'11), 2011
While both games and Multi-Objective Optimization (MOO) have been studied extensively in the literature, Multi-Objective Games (MOGs) have received less research attention. Existing studies deal mainly with mathematical formulations of the optimum. However, a definition and search for the representation of the optimal set, in the multi objective space,
Gideon Avigad +2 more
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While both games and Multi-Objective Optimization (MOO) have been studied extensively in the literature, Multi-Objective Games (MOGs) have received less research attention. Existing studies deal mainly with mathematical formulations of the optimum. However, a definition and search for the representation of the optimal set, in the multi objective space,
Gideon Avigad +2 more
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Multi-Objective Optimization of a Sorting System
2020 Winter Simulation Conference (WSC), 2020This paper addresses the optimization of a sorting system encountered in the semiconductor industry. Thesystem consists of parallel sorting machines and a material handler to transport materials to, from andbetween machines. The problem is decomposed into multiple subproblems. For each of these subproblemsheuristic methods are proposed.
Adan, Jelle +2 more
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A new hybrid memetic multi-objective optimization algorithm for multi-objective optimization
Information Sciences, 2018Abstract To deal with the multi-objective optimization problems (MOPs), a meta-heuristic based on an improved shuffled frog leaping algorithm (ISFLA) which belongs to memetic evolution is presented. For the MOPs, both diversity maintenance and searching effectiveness are crucial for algorithm evolution.
Jianping Luo +5 more
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Enhanced Efficiency in Multi-objective Optimization
Journal of Optimization Theory and Applications, 2013The authors introduce and study a generalized notion of proper efficiency in multi-objective optimization, denoted as \(\alpha\)-proper efficiency. After giving some preliminaries and definitions in Section 2, the main results are presented in Section 3. In particular, two characterizations of \(\alpha\)-proper efficiency are given: one in terms of the
Yuntao Jiang, Shujie Deng
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Multi-objective Optimal Control: An Overview
2007 IEEE International Conference on Control Applications, 2007Optimization techniques have been a crucial tool for designing control systems and for tuning controllers. The always increasing quality requirements for new products and consequently the natural advance in control system design have lead to the introduction of more than one design criterion, which will require in turn more sophisticated techniques to ...
Adrian Gambier, Essameddin Badreddin
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Introducing Robustness in Multi-Objective Optimization
Evolutionary Computation, 2006In optimization studies including multi-objective optimization, the main focus is placed on finding the global optimum or global Pareto-optimal solutions, representing the best possible objective values. However, in practice, users may not always be interested in finding the so-called global best solutions, particularly when these solutions are quite ...
Kalyanmoy Deb, Himanshu Gupta
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Automatic Configuration of Multi-objective Optimizers and Multi-objective Configuration
2019Heuristic optimizers are an important tool in academia and industry, and their performance-optimizing configuration requires a significant amount of expertise. As the proper configuration of algorithms is a crucial aspect in the engineering of heuristic algorithms, a significant research effort has been dedicated over the last years towards moving this
Bezerra, Leonardo C.T. +2 more
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2016
This chapter describes the Multi-objective approach used in this work. Moreover, a complete description on the algorithm components, such as, chromosomes, genes, etc., the evaluation mechanism and the investment simulator is presented.
António Daniel Silva +2 more
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This chapter describes the Multi-objective approach used in this work. Moreover, a complete description on the algorithm components, such as, chromosomes, genes, etc., the evaluation mechanism and the investment simulator is presented.
António Daniel Silva +2 more
+4 more sources
A Soft Approach to Multi-objective Optimization
2008Many combinatorial optimization problems require the assignment of a set of variables in such a way that an objective function is optimized. Often, the objective function involves different criteria, and it may happen that the requirements are in conflict: assignments that are good wrt. one objective may behave badly wrt. another.
Stefano Bistarelli +3 more
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