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Hyper multi-objective evolutionary algorithm for multi-objective optimization problems
Soft Computing, 2016Multi-objective optimization problems (MOPs) are very common in practice. To solve MOPs, many kinds of multi-objective evolutionary algorithms (MOEAs) are proposed. However, different MOEAs have different performances for different MOPs. Therefore, it is a time-consuming task to choose a suitable MOEA for a given problem.
Weian Guo, Ming Chen, Lei Wang, Qidi Wu
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Evolutionary Multi-objective Whale Optimization Algorithm
2019Whale Optimization Algorithm (WOA) is a recently proposed metaheuristic algorithm and achieved much attention of the researchers worldwide for its competitive performance over other popular metaheuristic algorithms. As a metaheuristic algorithm, it mimics the hunting behavior of humpback whale which uses its unique spiral bubble-net feeding maneuver to
Faisal Ahmed Siddiqi +1 more
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Evolutionary multi-objective optimization of Particle Swarm Optimizers
2007 IEEE Congress on Evolutionary Computation, 2007One issue in applying Particle Swarm Optimization (PSO) is to find a good working set of parameters. The standard settings often work sufficiently but don't exhaust the possibilities of PSO. Furthermore, a trade-off between accuracy and computation time is of interest for complex evaluation functions.
Christian Veenhuis +2 more
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Evolutionary Multi-objective Optimization of Business Processes
2006 IEEE International Conference on Evolutionary Computation, 2006Most of the current attempts for business process optimisation are manual without involving any formal automated methodology. This paper proposes a framework for multi-objective optimisation of business processes. The framework uses a generic business process model that is formally defined and specifies process cost and duration as objective functions.
Ashutosh Tiwari +2 more
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Clustering-Based Multi-objective Immune Optimization Evolutionary Algorithm
2012In everyday life, there are plentiful cases that we need to find good solutions such that risk, cost and many other factors are to be optimized. These problems are typical examples of multi-objective optimization problems. Evolutionary algorithms are often employed for solving it.
Tsang, WWP, Lau, HYK
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Advances in Evolutionary Multi-objective Optimization
2013Multi-objective evolutionary algorithms are a class of stochastic optimization Techniques that simulate biological evolution to solve problems with multiple (and often conflicting) objectives.
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Evolutionary Multi-Multi-Objective Optimization - EMMOO
2008In this chapter the recently introduced multi-Multi-ObjectiveOptimization Problem (m- MOOP) is described and a new evolutionary approach is suggested for its solution. The m-MOOP is a problem, which may be defined as a result of a demand to find solutions for several different multi-objective problems that are to share components.
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Advances in Evolutionary Multi-objective Optimization
2012Started during 1993-95 with three different algorithms, evolutionary multi-objective optimization (EMO) has come a long way in a quick time to establish itself as a useful field of research and application. Till to date, there exist numerous textbooks and edited books, commercial softwares dedicated to EMO algorithms, freely downloadable codes in most ...
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Evolutionary Multi-Objective Optimization in Finance
2007This chapter provides a brief introduction of the use of evolutionary algorithms in the solution of multi-objective optimization problems (an area now called “evolutionary multi-objective optimization”). Besides providing some basic concepts and a brief description of the approaches that are more commonly used nowadays, the chapter also provides some ...
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Evolutionary multi-objective optimization
European Journal of Operational Research, 2007Carlos A. Coello Coello +2 more
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