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Titlebook: Nature Inspired Cooperative Strategies for Optimization (NICSO 2008); Natalio Krasnogor,María Belén Melián-Batista,David Book 2009 Springe

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51#
發(fā)表于 2025-3-30 11:49:53 | 只看該作者
Two-Swarm PSO for Competitive Location Problems, leader-follower location problem which consists of determining optimal strategies for two competing firms, the leader and the follower, which make decisions sequentially. The follower has the objective of maximizing its market share, given the locations chosen by the leader. The leader optimization
52#
發(fā)表于 2025-3-30 12:44:46 | 只看該作者
Aerodynamic Wing Optimisation Using SOMA Evolutionary Algorithm,romising evolutionary algorithm SOMA with the intent to answer the question on optimal wing geometry. In the first part we describe the SOMA algorithm, its principle and configuration options. The second half is devoted to aerodynamic model of the wing, the optimisation process and results we obtain
53#
發(fā)表于 2025-3-30 19:47:05 | 只看該作者
Experimental Analysis of a Variable Size Mono-population Cooperative-Coevolution Strategy,rmulation of the problem to be solved as a cooperative task, where individuals collaborate or compete in order to collectively build a solution. Several strategies have been developed depending on the way the problem is shared into interdependent subproblems and the way coevolution occur (multipopul
54#
發(fā)表于 2025-3-30 21:57:23 | 只看該作者
Genetic Algorithm for Tardiness Minimization in Flowshop with Blocking,. In this problem there are no buffers between successive machines, that is, it is not allowed intermediate queues of jobs waiting in the system for their subsequent operations. To solve the problem, we propose a genetic algorithm that includes strategies like local search, a procedure to control ov
55#
發(fā)表于 2025-3-31 02:52:56 | 只看該作者
Landscape Mapping by Multi-population Genetic Algorithm,ching for all the local extrema of a given function with complex landscape. Mathematically, this multi-agent system is represented by a multi-population genetic algorithm (MPGA). Each population contains a set of agents that are binary coded chromosomes undergoing evolution with mutation and crossov
56#
發(fā)表于 2025-3-31 08:35:34 | 只看該作者
57#
發(fā)表于 2025-3-31 10:29:48 | 只看該作者
58#
發(fā)表于 2025-3-31 16:07:48 | 只看該作者
Terrain-Based Memetic Algorithms for Vector Quantizer Design,accelerated version of .-Means algorithm as a new local learning module. This approach requires the determination of a scale factor parameter?(.), which affects the local search performed by GAKM. The problem of auto-adapting the local search in GAKM, by adjusting the . parameter, is addressed in th
59#
發(fā)表于 2025-3-31 21:04:56 | 只看該作者
Cooperating Classifiers, experiences are made available to the group. We investigate the interaction between agent architecture, knowledge representation and communication strategy for cooperating agents in an unstable environment with many different niches and discuss application scenarios. Back-propagation artificial neu
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