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 court cases of the overseas convention on Swarm Intelligence, ICSI 2011, held in Chongqing, China, in June 2011. The 143 revised complete papers awarded have been rigorously reviewed and chosen from 298 submissions. The papers are equipped in topical sections on theoretical research of swarm intelligence algorithms, particle swarm optimization, purposes of pso algorithms, ant colony optimization algorithms, bee colony algorithms, novel swarm-based optimization algorithms, synthetic immune procedure, differential evolution, neural networks, genetic algorithms, evolutionary computation, fuzzy tools, and hybrid algorithms - for half I. issues addressed partially II are reminiscent of multi-objective optimization algorithms, multi-robot, swarm-robot, and multi-agent structures, information mining tools, computing device studying tools, function choice algorithms, trend popularity tools, clever keep an eye on, different optimization algorithms and functions, facts fusion and swarm intelligence, in addition to fish college seek - foundations and applications.

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

Example text

Definition 2 (Pareto optimality). For a general MO problem, a given solution y F (where F is the feasible solution space) is the Pareto optimality if, and only if there is no z F that dominates y. ∈ Correlative Particle Swarm Optimization for Multi-objective Problems 19 Definition 3 (Pareto front). The front obtained by mapping the Pareto optimal set (OS) into the objective space is called POF. JK POF = { f = ( f1 ( x)," , f M ( x)) | x ∈ OS } (2) The determination of a complete POF is a very difficult task, owing to the presence of a large number of suboptimal Pareto fronts.

2. The solution y is strictly better than z in at least one objective, or fi(y)

EXA-improvement () % local search on EXA % End while Report the obtained non-dominated solutions in EXA. End Fig. 1. The main procedure of HMOPSO Step 1. If |EXA| = 1, go to Step 2; otherwise, go to Step 3. Step 2. Perturb the single solution in EXA for nEXA times to generate other nEXA new solutions. Step 3. Randomly select two solutions in EXA, and use the simulated binary crossover (SBX) operator to generate two offspring solutions. Select the best non-dominated one as the new solution. Repeat this procedure until nEXA new solutions are obtained.

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