The Clustered Shortest-Path Tree Problem (CluSPT) plays an important role in various types of optimization problems in real-life. Recently, some multifactorial evolutionary algorithms (MFEAs) have been introduced to d...
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The Clustered Shortest-Path Tree Problem (CluSPT) plays an important role in various types of optimization problems in real-life. Recently, some multifactorial evolutionary algorithms (MFEAs) have been introduced to deal with the CluSPT, but these researches still have some shortcomings, such as evolution operators only perform on complete graphs and huge resource consumption for finding the solution on large search spaces. To overcome these limitations, this paper describes an MFEA-based approach to solve the CluSPT. The proposed algorithm utilizes Dijkstra?s algorithm to construct the spanning trees in clusters while using evolutionary operators for building the spanning tree connecting clusters. This approach takes advantage of both exact and approximate algorithms, so it enables the algorithm to function efficiently on complete and sparse graphs alike. Furthermore, evolutionary operators such as individual encoding and decoding methods are also designed with great consideration regarding performance and memory usage. We have included proof of the repairing method?s efficacy in ensuring all solutions are valid. We have conducted tests on various types of Euclidean instances to assess the effectiveness of the proposed algorithm and methods. Experiment results point out the effectiveness of the proposed algorithm existing heuristic algorithms in most of the test cases. The impact of the proposed MFEA was analyzed, and a possible influential factor that may be useful for further study was also pointed out.
As a novel and representative multi-task optimisation (MTO) paradigm, multi-factorial evolutionaryalgorithm (MFEA) can solve multiple self-contained tasks simultaneously. Its overall performance highly depends on con...
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As a novel and representative multi-task optimisation (MTO) paradigm, multi-factorial evolutionaryalgorithm (MFEA) can solve multiple self-contained tasks simultaneously. Its overall performance highly depends on control parameters. The aim of this research work is to analyse three parameters, namely, probability of individual learning, probability of intra-crossover and probability of inter-crossover, controlled by the user. Experimental results on MTO problems demonstrate the superiority of MFEA with a smaller probability of individual learning in a fair competitive environment. While the influence of probabilities of intra-crossover and inter-crossover is unpredictable based on the task's features, the basic selection principle and the optimal value are provided based on massive simulated data.
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