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Architecting systems designed to elicit group-level behavior beyond the capability of any single agent, however, demands a labor and experimentation-intensive cycle on the part of the programmer. As part of a system to evolve swarm behaviors, we have developed a mixed radix fitness function to overcome the problems encountered with typical fitness functions when used in a multi-objective optimization...
This work presents an optimization approach for the broadcast operation in MANETs based on the DFCN protocol. Such approach involves a multi-objective optimization that has been tackled through the cooperation of a team of evolutionary algorithms. The proposed optimization model is a hybrid algorithm that combines a parallel island-based scheme with a hyperheuristic approach. The model includes an...
In this paper, we propose a new multi-objective optimization approach based the clonal selection principle which is from an artificial immune system. Our approach uses the cluster method in the memory cell set of the clonal selection principle to renew and eliminate antibody; the non-uniform mutation operator is employed to the multiplicity of population. This algorithm cannot promote to individual...
The purpose of this research is to develop an effective task assignment algorithm for multiple unmanned underwater vehicles (UUVs) to reacquaint multiple targets. This algorithm is specifically designed for the underwater environment where vehicles typically have dissimilar starting and ending locations. Besides the objective of minimizing the total distance of multiple vehicles, the objectives of...
This paper presents an evolutionary approach to the finding of maxmin-fair solutions of the general network resource allocation problem. From the definition of maxmin-fairness, a general relation among real points is derived that can be used to replace the role of the Pareto-dominance relation in evolutionary multi-objective search algorithms. The approach has been demonstrated by modifying the SPEA2...
With the fast development of AI planning, planning technology has been widely applied to robotics and automated cybernetics. Many researchers pay more and more attention to the uncertainty in AI planning, probabilistic planning is a important branch of uncertainty planning. In realistic domains, probabilistic planning often involves multiple objectives, where it aims to generate optimal set of plans...
Constrained optimization problems (COPs) are a kind of mathematic programming problem frequently encountered in the disciplines of science and engineering application. After analyzing weaknesses of existing constrained optimization evolutionary algorithms (COEAs), a novel improved algorithm called complex-GA, which converts COPs into multi-objective optimization problems (MOPs) and effectively combines...
Solving multi-objective optimization problem using evolutionary algorithm has attracted much attentions and the great progresses have been made in the past decades. The whole history of the research on multi-objective evolutionary algorithm (MOEA) is divided into three periods. After the features of the researches of MOEA and the research achievements are briefly reviewed, the new progresses of MOEA...
Evolutionary gradient search is a hybrid algorithm that exploits the complementary features of gradient search and evolutionary algorithm to achieve a level of efficiency and robustness that cannot be attained by either techniques alone. Unlike the conventional coupling of local search operators and evolutionary algorithm, this algorithm follows a trajectory based on the gradient information that...
Optimizing reverse logistics network is important to the sustainable development of logistics. In this paper, we established a reverse logistics network multi-objective optimization model which fully considered environment effect and the waste recycling factors, such as locations, frequency, quality and quantity. This model can ensure that both the whole cost of network and the negative impact on...
Product family design has been recognized as an effective method to satisfy diverse customer's demands cost-effectively. The success of the resulting product family often relies on properly resolving the inherent tradeoff between commonality across the family and performance loss compared to individual design. In this paper, a modified genetic algorithm using dynamic weighted aggregation is proposed...
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