The evolutionaryalgorithms (EAs) became more and more important in solving NP-hard problems in recent years. The representation of specific problems in EAs is very important and it has a great influence on the perfor...
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The evolutionaryalgorithms (EAs) became more and more important in solving NP-hard problems in recent years. The representation of specific problems in EAs is very important and it has a great influence on the performance of EAs. The constraint satisfaction problems (CSPs) are typical NP-hard problems and the representation of CSPs can be traditionally divided into two types, namely the direct and indirect representations. The variables in direct representation represent the actual values that they can take, and can be evaluated directly. Whereas in indirect representation, a specific permutation is assigned to variables, and the individual is incapable of being evaluated without a decoder. In order to take advantage of both representations to enforce the ability of EAs in solving CSPs, we propose a combination of these two representations in this article. The minimum conflict decoder is employed to transform indirect representation to direct representation and several new behaviors are designed for agents in multiagent evolutionary algorithms. In experiments, 250 benchmark binary CSPs and 79 graph coloring problems are tested. The comparisons among the direct, indirect and the combined representation methods are conducted. Experimental results illustrate that the method of combined representation outperforms the two other methods.
From the viewpoint of decision making process, it brings inconveniences for decision makers to select one (few) proper solution(s). Thus we propose preference oriented two-layered multiagent evolutionary algorithm (TL...
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ISBN:
(纸本)9783642172977
From the viewpoint of decision making process, it brings inconveniences for decision makers to select one (few) proper solution(s). Thus we propose preference oriented two-layered multiagent evolutionary algorithm (TL-MAEA) to meet customers' needs. The algorithm has a structure of two layers: in the top layer, preference relations among multiple objectives are calculated through interactions with the decision maker;while in the bottom layer, MAEA is employed to obtain the optimal solution corresponding to the preference relations. In the experimental, 12 benchmark problems are used to test the algorithm. The results show that the proposed algorithm is effective.
This paper investigates the network coding resource minimization problem in the context of dynamic network environment. As a combination of multiagent systems and evolutionaryalgorithm, multiagentevolutionary algori...
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ISBN:
(纸本)9781510829039
This paper investigates the network coding resource minimization problem in the context of dynamic network environment. As a combination of multiagent systems and evolutionaryalgorithm, multiagent evolutionary algorithm（MAEA） is adapted for the above NP-hard problem. Simulation results demonstrate that the proposed MAEA outperforms a number of state-of-the-art evolutionaryalgorithms with respect to the solution quality.
In this paper, a new representation for resource-constrained project scheduling problems (RCPSPs), namely moving block sequence (MBS), is proposed. In RCPSPs, every activity has fixed duration and resource demands, th...
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In this paper, a new representation for resource-constrained project scheduling problems (RCPSPs), namely moving block sequence (MBS), is proposed. In RCPSPs, every activity has fixed duration and resource demands, therefore, it can be modelled as a rectangle block whose height represents the resource demand and width the duration. Naturally, a project that consists of N activities can be represented as the permutation of N blocks that satisfy the precedence constraints among activities. To decode an MBS to a valid schedule, four move modes are designed according to the situations that how every block can be moved from its initial position to an appropriate location that can minimise the makespan of the project. Based on MBS, the multiagent evolutionary algorithm (MAEA) is used to solve RCPSPs. The proposed algorithm is labelled as MBSMAEA-RCPSP, and by comparing with several state-of-the-art algorithms on benchmark J30, J60, J90 and J120, the effectiveness of MBSMAEA-RCPSP is clearly illustrated.
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