Proceedings Article10.1109/ROBIO.2004.1521774
Research on A New Multiobjective Combinatorial Optimization Algorithm
Qin yong-fa,Zhao Mingyang +1 more
- 22 Aug 2004
- pp 187-191
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TL;DR: After modeling the constrained multiobjective combinatorial optimization problem, a new optimization algorithm is presented in detail that hybridises the simulated annealing algorithm with the genetic algorithm to improve the global searching ability while maintaining parallel computing ability.
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Abstract: A real world engineering design problem usually has multiple conflicting objectives, which can easily lead to difficulty in optimizing these objectives at the same time. Multiobjective combinatorial optimization is not only an open theory problem, it also has important practical significance. After modeling the constrained multiobjective combinatorial optimization problem, a new optimization algorithm is presented in detail. The algorithm is different from existing multiobjective evolutionary algorithms in three aspects. The first is the two-layer encoding method. The second is that it hybridises the simulated annealing algorithm with the genetic algorithm to improve the global searching ability while maintaining parallel computing ability. The third is the decision making mechanism to evaluate candidate solutions with several design objectives. A numerical example study shows that the proposed algorithm is capable of dealing with multiobjective combinatorial optimization problems
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Citations
Evolutionary algorithm based on simulated annealing for the multi-objective optimization of combinatorial problems
Elias D. Nino Ruiz,Henry Nieto Parra,Anangelica Isabel Chinchilla Camargo +2 more
- 01 Jan 2013
TL;DR: This paper states a novel hybrid-metaheuristic based on the Theory of Deterministic Swapping, Theory of Evolution and Simulated Annealing Meta-heuristic for simulations of Annealing meta-heuristics for reinforcement learning.
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Evolutionary Algorithm based on the Automata Theory for the Multi-objective Optimization of Combinatorial Problems
TL;DR: This paper states a novel, Evolutionary Metaheuristic Based on the Automata Theory (EMODS) for the multiobjective optimization of combinatorial problems, which uses the natural selection theory in order to explore the feasible solutions space of a combinatorsial problem.
Multi-objective reinforcement learning algorithm and its improved convergency method
Zhao Jin,Zhang Huajun +1 more
- 21 Jun 2011
TL;DR: A multi-objective reinforcement learning algorithm (MORLA) is proposed and simultaneous perturbation stochastic approximation (SPSA) is used to improve the convergence of it and the hybrid algorithm accelerates the learning speed of reinforcement learning.
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EVOLUTIONARY ALGORITHM BASED ON SIMULATED ANNEALING FOR THE MULTI-OBJECTIVE OPTIMIZATION OF COMBINATORIAL PROBLEMS EMSA: Hybrid Metaheuristic based on Genetic Algorithms, Simulated Annealing and Deterministic Swapping
Elias D. Nino Ruiz,Henry Nieto Parra,Anangelica Isabel Chinchilla Camargo +2 more
- 01 Jan 2013
TL;DR: This paper states a novel hybrid-metaheuristic based on the Theory of Deterministic Swapping, Theory of Evolution and Simulated Annealing Meta-heuristic for the multi-objective optimization of combinatorial prob- lems, an improvement of MODS algorithm.
1
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