Titlebook: Metaheuristics for Dynamic Optimization; Enrique Alba,Amir Nakib,Patrick Siarry Book 2013 Springer-Verlag Berlin Heidelberg 2013 Computati

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河流

沉思的鱼

Insect Swarm Algorithms for Dynamic MAX-SAT Problems,d wasp swarm optimization algorithms, which are based in the real life behavior of ants and wasps, respectively. The algorithms are applied to several sets of static and dynamic MAX-SAT instances and are shown to outperform the greedy hill climbing and simulated annealing algorithms used as benchmarks.

贪心

Performance Analysis of Dynamic Optimization Algorithms, approaches developed to address these problems. The goal of this chapter is to present the different tools and benchmarks to evaluate the performances of the proposed algorithms. Indeed, testing and comparing the performances of a new algorithm to the different competing algorithms is an important

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诱惑

Dynamic Function Optimization: The Moving Peaks Benchmark,ete restart of the optimization algorithm may not be warranted. In those cases, it is meaningful to apply optimization algorithms that can accommodate change. In the recent past, many researchers have contributed algorithms suited for dynamic problems. To facilitate the comparison between different

Ingredient

SRCS: A Technique for Comparing Multiple Algorithms under Several Factors in Dynamic Optimization Pthe researcher usually tests many algorithms, with several parameters, under different problems. The situation is even more complex when dynamic optimization problems are considered, since additional dynamism-specific configurations should also be analyzed (e.g. severity, frequency and type of the c

轻而薄

Dynamic Combinatorial Optimization Problems: A Fitness Landscape Analysis,lems thanks to a variety of empirical studies as well as some theoretical results. In the field of evolutionary dynamic optimization very few studies exist to date that explicitly analyse the impact of these elements on the algorithm’s performance. In this chapter we utilise the fitness landscape me

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