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Application of multi-objective fruit fly optimisation algorithm based on population Manhattan distance in distribution network reconfiguration

Tang Minan, Zhang Kaiyue, Wang Qianqian, Cheng Haipeng, Yang Shangmei, Du Hanxiao

Archives of Electrical Engineering

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2021

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Vol. 70, nr 2

307--323

EN

In order to optimise the operation state of the distribution network in the presence of distributed generation (DG), to reduce network loss, balance load and improve power quality in the distribution system, a multi-objective fruit fly optimisation algorithm based on population Manhattan distance (pmdMOFOA) is presented. Firstly, the global and local exploration abilities of a fruit fly optimisation algorithm (FOA) are balanced by combining population Manhattan distance (PMD) and the dynamic step adjustment strategy to solve the problems of its weak local exploration ability and proneness to premature convergence. At the same time, Chebyshev chaotic mapping is introduced during position update of the fruit fly population to improve ability of fruit flies to escape the local optimum and avoid premature convergence. In addition, the external archive selection strategy is introduced to select the best individual in history to save in external archives according to the dominant relationship amongst individuals. The leader selection strategy, external archive update and maintenance strategy are proposed to generate a Pareto optimal solution set iteratively. Lastly, an optimal reconstruction scheme is determined by the fuzzy decision method. Compared with the standard FOA, the average convergence algebra of a pmdMOFOA is reduced by 44.58%. The distribution performance of non-dominated solutions of a pmdMOFOA, MOFOA, NSGA-III and MOPSO on the Pareto front is tested, and the results show that the pmdMOFOA has better diversity. Through the simulation and analysis of a typical IEEE 33-bus system with DG, load balance and voltage offset after reconfiguration are increased by 23.77% and 40.58%, respectively, and network loss is reduced by 57.22%, which verifies the effectiveness and efficiency of the proposed method.

2

The optimal design of fractional sliding mode control based on multi-objective genetic algorithms for a two-link flexible manipulator

Pouya M., Pashaki P. V.

Advances in Science and Technology. Research Journal

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2017

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Vol. 11, no 3

56--65

EN

In this paper a novel optimal approach of control strategy is introduced by applying fractional calculus in the structure of sliding mode control for a range of dynamics system liable to ambiguity. So, a fractional sliding mode control was designed for dynamics of the two-link rigid-flexible manipulator. Furthermore, a multi-objective genetic algorithm was proposed in order to find the ideal variable structure of the sliding mode control. Optimal variables were achieved by the optimization of the conventional sliding mode control. Then the performance of both the conventional and the fractional sliding mode control were compared with respect to optimal variables. Results indicated that by applying the optimized fractional sliding mode control, the system’s error was significantly reduced consequently tracking the desired value was done with a higher degree of accuracy and a smoother control action was achieved.

3

Graph Theory Approach to Transportation Systems Design and Optimization

Guze S.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

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2014

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Vol. 8 no. 4

572--578

EN

The main aim of the paper is to present graph theory parameters and algorithms as tool to analyze and to optimise transportation systems. To realize these goals the 0-1 knapsack problem solution by SPEA algorithm, methods and procedures for finding the minimal spanning tree in graphs and digraphs, domination parameters problems accurate to analyse the transportation systems are introduced and described. Possibility of application of graph theory algorithms and parameters to analyze exemplary transportation system are shown.

4

Unsupervised and Supervised Learning Approaches Together for Microarray Analysis

Saha I., Maulik U., Bandyopadhyay S., Plewczynski D.

Fundamenta Informaticae

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2011

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Vol. 106, nr 1

45-73

EN

In this article, a novel concept is introduced by using both unsupervised and supervised learning. For unsupervised learning, the problem of fuzzy clustering in microarray data as a multiobjective optimization is used, which simultaneously optimizes two internal fuzzy cluster validity indices to yield a set of Pareto-optimal clustering solutions. In this regards, a new multiobjective differential evolution based fuzzy clustering technique has been proposed. Subsequently, for supervised learning, a fuzzy majority voting scheme along with support vector machine is used to integrate the clustering information from all the solutions in the resultant Pareto-optimal set. The performances of the proposed clustering techniques have been demonstrated on five publicly available benchmark microarray data sets. A detail comparison has been carried out with multiobjective genetic algorithm based fuzzy clustering, multiobjective differential evolution based fuzzy clustering, single objective versions of differential evolution and genetic algorithm based fuzzy clustering as well as well known fuzzy c-means algorithm. While using support vector machine, comparative studies of the use of four different kernel functions are also reported. Statistical significance test has been done to establish the statistical superiority of the proposed multiobjective clustering approach. Finally, biological significance test has been carried out using a web based gene annotation tool to show that the proposed integrated technique is able to produce biologically relevant clusters of coexpressed genes.