Multi- or many-objective evolutionary algorithms\(MOEAs\), especially the decomposition-based MOEAs have been widely concerned in recent years. The decomposition-based MOEAs emphasize convergence and diversity in a si...
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A guide person evolutionary game of evacuation model based on improved PSO algorithm is established, which optimizes the moving intention position of the guide person and the evacuees. The simulation results rational ...
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A guide person evolutionary game of evacuation model based on improved PSO algorithm is established, which optimizes the moving intention position of the guide person and the evacuees. The simulation results rational reflect the evacuation process in emergency situations. The results indicate that with the increase of the number of guide persons, the evacuation time under different imitation coefficients have a trend of decreases first and then increases. After reaching of the minimum evacuation time, with the continue increase in the number of guide persons, on the contrary the overall evacuation time increase. From view of the cooperation probability, when the guide persons less than 8, the evacuees maintain a cooperative attitude, with the increase in the number of guide persons, the cooperation probability all have varying degrees of decrease under four kinds of imitation coefficients. When the number of guide persons is 6, the bigger the shortest path coefficient is, the longer the whole evacuation time is. When the value of C1 is fixed, the larger the imitation coefficient is, the smaller the evacuation time is; When the number of guide persons increase to 50, the larger the value ofC1, the smaller the overall evacuation time instead; when the C1 value is fixed, the larger the imitation coefficient, the larger the evacuation time. In the case of a small number of guide persons, the overall evacuation efficiency can be increased by increasing the range of influence of the guide person.
—Researches have shown difficulties in obtaining proximity while maintaining diversity for many-objective optimization problems. Complexities of the true Pareto front pose challenges for the reference vector-based al...
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Large-scale optimization problems that involve thousands of decision variables have extensively arisen from various industrial areas. As a powerful optimization tool for many real-world applications, evolutionary algo...
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This paper describes the N-Tuple Bandit evolutionary Algorithm (NTBEA), an optimisation algorithm developed for noisy and expensive discrete (combinatorial) optimisation problems. The algorithm is applied to two game-...
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Many-objective evolutionary algorithms (MOEAs), especially the decomposition-based MOEAs, have attracted wide attention in recent years. Recent studies show that a well designed combination of the decomposition method...
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MSC Codes 80M50, 90C27This research proposes a novel indicator-based hybrid evolutionary approach that combines approximate and exact algorithms. We apply it to a new bi-criteria formulation of the travelling thief pr...
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Inverted Generational Distance (IGD) has been widely considered as a reliable performance indicator to concurrently quantify the convergence and diversity of multi-and manyobjective evolutionary algorithms. In this pa...
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In an attempt to solve the problem of finding a set of multiple unique modular robotic designs that can be constructed using a given repertoire of modules to perform a specific task, a novel synthesis framework is int...
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In an attempt to solve the problem of finding a set of multiple unique modular robotic designs that can be constructed using a given repertoire of modules to perform a specific task, a novel synthesis framework is introduced based on design optimization concepts and evolutionary algorithms to search for the optimal solution. The discrete optimization procedure is based on the use of assembly incidence matrix to represent modular robotic designs. Fitness function is formulated for each task separately to incorporate task-specific performance evaluation criteria. The fitness of every design is measured in simulation. Solution evaluation can be carried out in parallel in order to reduce synthesis time, because evaluating a certain design is independent of evaluating other designs. The feasibility of this approach is demonstrated by several examples.
This report presents benchmarking results of the latest version of the Hill-Valley evolutionary Algorithm (HillVallEA) on the CEC2013 niching benchmark suite. The benchmarking follows restrictions required by the GECC...
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