Titlebook: Advances in Swarm and Computational Intelligence; 6th International Co Ying Tan,Yuhui Shi,Andries Engelbrecht Conference proceedings 2015 S

Coeval

Bean Optimization Algorithm Based on Negative Binomial Distributionroposed many nature-inspired optimization algorithms. When solving some complex problems which cannot be solved by the traditional optimization algorithms easily, the nature-inspired optimization algorithms have their unique advantages. Inspired by the transmission mode of seeds, a novel evolutionar

hieroglyphic

On the Application of Co-Operative Swarm Optimization in the Solution of Crystal Structures from X-Rthod does not need essential effort for its adjustment to the problem in hand but demonstrates high performance. This algorithm is compared with a sequential two-level genetic algorithm, a multi-population parallel genetic algorithm and a self-configuring genetic algorithm as well as with two proble

Paradox

Swarm Diversity Analysis of Particle Swarm Optimizationnalyses the reasons leading to the loss of swarm diversity by computing and analyzing of the probabilistic characteristics of the learning factors in PSO. It also provides the relationship between the loss of swarm diversity and the probabilistic distribution and dependence of learning parameters. E

capsaicin

A Self-learning Bare-Bones Particle Swarms Optimization Algorithm First, the expectation of Gaussian distribution in the updating equation is controlled by an adaptive factor, which makes particles emphasize on the exploration in earlier stage and the convergence in later stage. Second, SLBBPSO adopts a novel mutation to the personal best position (.) and the glo

廣口瓶

Improved DPSO Algorithm with Dynamically Changing Inertia Weightptimization algorithm and improve the optimization accuracy and stability of standard PSO algorithm. However, the accuracy of DPSO for solving the multi peak function will be obviously decreased. To solve the problem, we introduce the linearly decreasing inertia weight strategy and the adaptively ch

粘

FID

A Fully-Connected Micro-extended Analog Computers Array Optimized by Particle Swarm Optimizermatical model and two uEAC extensions with minus-feedback and multiplication-feedback, respectively. Then a fully-connected uEACs array is proposed to enhance the computational capability, and to get an optimal uEACs array structure for specific problems, a comprehensive optimization strategy based

聽寫

A Population-Based Clustering Technique Using Particle Swarm Optimization and K-Means. However, the performance of these hybrid clustering methods have not been extensively analyzed and compared with other competitive clustering algorithms. In the paper, five existing PSOs, which have shown promising performance for continuous function optimization, are hybridized separately with K-

moratorium

A Novel Boundary Based Multiobjective Particle Swarm Optimizationhes the border of the objective space unlike other current proposals to look for the Pareto solution set to solve such problems. In addition, we apply the proposed method to other particle swarm optimization variants, which indicates the strategy is highly applicatory. The proposed approach is valid

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