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

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Antecedents of Non-Provocative Defencethod 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

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Asymptotic Relative Efficiency,nalyses 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

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Two-Sample Rank Procedures for Location, 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

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https://doi.org/10.1007/978-1-4612-2280-4al damage detection (SDD). The improved NMA chooses parts of subplanes of the .-simplex for optimization, a two-step method uses modal strain energy based index (MSEBI) to locate damage firstly, and both of them can reduce the computational cost of the basic PSO-Nelder-Mead (PSO-NM). An index of sol

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Social media as influence factor of qualityhes 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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只看該作者 · 發(fā)表于 2025-3-25 00:12:56

Utilizing Abstract Phase Spaces in Swarm Design and ValidationWe introduce a swarm design methodology. The methodology uses a seven step process involving a high-level phase space to map the desired goal to a set of behaviors, castes, deployment schedules, and provably optimized strategies. We illustrate the method on the stick-pulling task.

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