Titlebook: Advances in Swarm Intelligence; 5th International Co Ying Tan,Yuhui Shi,Carlos A. Coello Coello Conference proceedings 2014 Springer Intern

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Lawrence R. Pomeroy,James J. Albertss global-coupling and likely to stop at local optima rather than the global one. This paper analyses PSO topology with complex network theory and proposes two approaches to improve PSO performance. One improvement is PSO with regular network structure (RN-PSO) and another is PSO with random network

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https://doi.org/10.1007/978-94-007-5914-5e whole population is divided into a number of sub-swarms, in which the learning probability is employed to influence the exemplar of each individual and the center position of the best experience found so far by all the sub-swarms is also used to balance exploration and exploitation. Each particle

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Concepts of Matter in Science Educationard matrices of given orders are found via complete enumeration in the literature, but the searches are too computationally intensive when the orders are large. This paper introduces a search method for circulant partial Hadamard matrices by using natural heuristic algorithm. Slightly deviated from

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Concepts of Matter in Science Educationon of unconventional algorithmic approaches based on natural computation. Ant Colony Optimization (ACO) technique is one of the popular unconventional optimization technique to solve this problem. In this paper, we propose High Performance Ant Colony Optimizer (HPACO) which modifies conventional ACO

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PHuNAC Model: Emergence of Crowd’s Swarm Behavioratterns. In order to validate our approach, we compared our system with real data. The conducted experiments show that the model is consistent with the various emergent behaviors and thus it provides realistic simulated pedestrian’s behavior.

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Improving Enhanced Fireworks Algorithm with New Gaussian Explosion and Population Selection Strategi high-quality solutions to have high probabilities of entering the next generation without incurring high computational cost. Numerical experiments show that the IEFWA algorithm outperforms EFWA on a set of benchmark function optimization problems.

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A Unified Matrix-Based Stochastic Optimization Algorithmare inspired by the elementary matrix transformations, all of which have none latent meanings. Experiments with real-coded genetic algorithm, particle swarm optimization and differential evolution illustrate its promising performance and potential.

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A Population-Based Extremal Optimization Algorithm with Knowledge-Based Mutationd to PID parameter tuning. The simulation results show that the proposed algorithm is characterized by high response speed, small overshoot and steady-state error, and obtains satisfactory control effect.

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A Magnetotactic Bacteria Algorithm Based on Power Spectrum for Optimizationin the algorithm. Its performance is tested on 8 standard functions problems and compared with the other two popular optimization algorithms. Experimental results show that the PSMBA is effective in optimization problems and has good and competitive performance.