Faculty of Symbiotic Systems Science, Fukushima University, Fukushima City, 960-1296, Japan This paper focuses on efficiency of simulation-based optimization for container terminal operation system. Firstly, the discrete-event simulation model of multimodal operation system on container terminal was presented with the queuing network, for which the mathematical optimization model of resource al...

In global optimization, when the evaluation of the target function is costly, the usual strategy is to learn a surrogate model for the target function and replace the initial optimization by the optimization of the model. Gaussian processes have been widely used since they provide an elegant way to model the fitness and to deal with the explorationexploitation trade-off in a principled way. Sev...

The topic of multimodal function optimization, where the aim is to locate more than one solution, has attracted a growing interest especially in the evolutionary computing research community. To experimentally evaluate the strengths and weaknesses of multimodal optimization algorithms, it is important to use test functions representing different characteristics and of various levels of difficul...

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Interest in multimodal optimization function is expanding rapidly since real-world optimization problems often require the location of multiple optima in the search space. In this context, fitness sharing has been used widely to maintain population diversity and permit the investigation of many peaks in the feasible domain. This paper reviews various strategies of sharing and proposes new recom...

Scenario analysis often is used to determine bottlenecks in multimodal transportation and logistics chains. Bottleneck-analysis itself is a process related approach to identify shortages in multimodal transportation and logistics supply chains, and concerned with analysis of resource planes, optimization of multimodal transportation chains, consideration of timeliness and concurrency using reso...

Multiobjective selection operators are a popular and straightforward tool for preserving diversity in evolutionary optimization algorithms. One application area where diversity is essential is multimodal optimization with its goal of finding a diverse set of either globally or locally optimal solutions of a single-objective problem. We therefore investigate multiobjective selection methods that...

This paper presents simple analytical models of genetic algorithms which are commonly used in multimodal function optimization. The methodology for constructing the models is similar throughout the study. The predictive value of each model is veriied by running the corresponding genetic algorithm on various multimodal functions of varying complexity.

Today, in computer science, a computational challenge exists in finding a globally optimized solution from an enormously large search space. Various meta-heuristic methods can be used for finding the solution in a large search space. These methods can be explained as iterative search processes that efficiently perform the exploration and exploitation in the solution space. In this context, thre...