Download Advances in Swarm Intelligence: Second International by Li Nie, Liang Gao, Peigen Li, Xiaojuan Wang (auth.), Ying PDF

By Li Nie, Liang Gao, Peigen Li, Xiaojuan Wang (auth.), Ying Tan, Yuhui Shi, Yi Chai, Guoyin Wang (eds.)

The two-volume set (LNCS 6728 and 6729) constitutes the refereed lawsuits of the overseas convention on Swarm Intelligence, ICSI 2011, held in Chongqing, China, in June 2011. The 143 revised complete papers provided have been rigorously reviewed and chosen from 298 submissions. The papers are prepared in topical sections on theoretical research of swarm intelligence algorithms, particle swarm optimization, functions of pso algorithms, ant colony optimization algorithms, bee colony algorithms, novel swarm-based optimization algorithms, man made immune process, differential evolution, neural networks, genetic algorithms, evolutionary computation, fuzzy tools, and hybrid algorithms - for half I. issues addressed partially II are comparable to multi-objective optimization algorithms, multi-robot, swarm-robot, and multi-agent platforms, information mining equipment, computing device studying equipment, characteristic choice algorithms, trend acceptance tools, clever keep watch over, different optimization algorithms and functions, facts fusion and swarm intelligence, in addition to fish tuition seek - foundations and applications.

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Extra info for Advances in Swarm Intelligence: Second International Conference, ICSI 2011, Chongqing, China, June 12-15, 2011, Proceedings, Part II

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Therefore, in this paper we do not follow the canonical flight equations, but prefer to adopt a new strategy based on the SBX operator of GA. Since there have been many research report with respect to the parameters of the SBX operator, it is reasonable to follow the suggested setting, and thus there is no parameters to be tuned in the adopted update mechanism. For each particle i, this strategy has two simple steps: (1) randomly select a pbest from PBA[i], use the crossover operator to generate two offspring solutions from the particle i and its A PSO-Based Hybrid Multi-Objective Algorithm 29 selected pbest, and then randomly select a gbest from the EXA; and (2) use the SBX crossover operator to generate two offspring solutions from the selected pbest and gbest, and then select the best one based on Pareto dominance as the new particle.

Ri denotes the SRs obtained by MOGEP for the objective of Fi (i = 1,…,4) simultaneously. Take the result on instance 1-1 for example. The solutions generated on instance 1-1 with the MOGEP-created SRs R1, R2, R3 and R4 are X1={5167, 1557, 3218, 385}, X2={5167, 2397, 1338, 368}, X3={5167, 2428, 1330, 385}, and X4={5167, 2292, 2601, 234}, respectively. The solution generated on instance 1-1 with the GEPcreated SR R-F1 is X-F1={5167, 2447, 3422, 675}. It is optimal with respect to the objective of makespan in the comparison with the solutions generated by other GEPcreared SRs on instance 1-1, whereas it is dominated by Xi (i=1, 2, 3, 4).

00 Fig. 2. 00 Fig. 3. 0 2 -2 -8 -8 -10 -10 -12 -12 -20 -19 -18 -17 -16 -15 -14 -20 -19 -18 -17 Fig. 4. Pareto fronts produced by different algorithms for ZDT6 and KUR -16 -15 -14 32 X. Wang and L. 2 -4 -2 0 2 4 6 -4 -2 0 2 4 6 Fig. 5. Pareto fronts produced by different algorithms for Deb2 and KITA 4 Conclusion In this paper, we investigated the improvement to the canonical MOPSO algorithm and proposed three main strategies. First, the traditional update equations for particles’ positions are replaced by a new particle flight mechanism that is based on the crossover operator in GA.

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