Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization最新文献

Introductory Chapter: Nature-Inspired Methods for Stochastic, Robust, and Dynamic Optimization 导论章:随机、鲁棒和动态优化的自然启发方法

Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization Pub Date : 2018-07-18 DOI: 10.5772/INTECHOPEN.78009

E. Osaba, J. Ser

{"title":"Introductory Chapter: Nature-Inspired Methods for Stochastic, Robust, and Dynamic Optimization","authors":"E. Osaba, J. Ser","doi":"10.5772/INTECHOPEN.78009","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.78009","url":null,"abstract":"Optimization is one of the most studied fields in the wide field of artificial intelligence. Hundreds of studies published year after year focus on solving many diverse problems of this kind by resorting to a vast spectrum of solvers. Within this class of problems, several problem flavors can be identified depending on the characteristics of their constituent fitness functions and support of their optimization variables, such as linear, continuous or combinatorial. Efficiently tackling such optimization problems requires huge computational resources, especially when the formulated problem at hand represents complex real-world situations with hundreds of variables and constraints. For these reasons and due to the inherently practical utility of optimization algorithms, very heterogeneous problem-solving approaches have been developed by the community over the last decades for their application to these problems. From a general perspective, optimization methods can be classified as exact, heuristics, and metaheuristics. In this chapter, the focus is placed on the latter two families, in particular in those algorithmic variants where biological processes observed in nature have lied at the motivating core of the operators underlying their search mechanisms. In other words, we will center our attention on Nature-Inspired methods for efficient optimization and problem solving.","PeriodicalId":408183,"journal":{"name":"Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124556050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Brief Survey on Intelligent Swarm-Based Algorithms for Solving Optimization Problems 基于智能群的优化问题求解算法综述

Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization Pub Date : 2018-07-18 DOI: 10.5772/INTECHOPEN.76979

S. M. Lim, K. Y. Leong

引用次数: 15

Evaluation of Non-Parametric Selection Mechanisms in Evolutionary Computation: A Case Study for the Machine Scheduling Problem 演化计算中非参数选择机制的评估:以机器调度问题为例

Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization Pub Date : 2018-07-18 DOI: 10.5772/INTECHOPEN.75984

M. Dulebenets

{"title":"Evaluation of Non-Parametric Selection Mechanisms in Evolutionary Computation: A Case Study for the Machine Scheduling Problem","authors":"M. Dulebenets","doi":"10.5772/INTECHOPEN.75984","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.75984","url":null,"abstract":"Evolutionary Algorithms have been extensively used for solving stochastic, robust, and dynamic optimization problems of a high complexity. Selection mechanisms play a very important role in design of Evolutionary Algorithms, as they allow identifying the parent chromosomes, that will be used for producing the offspring, and the offspring chromosomes, that will survive in the given generation and move on to the next generation. Selection mechanisms, reported in the literature, can be classified in two groups: (1) parametric selection mechanisms, and (2) non-parametric selection mechanisms. Unlike parametric selection mechanisms, non-parametric selection mechanisms do not have any parameters that have to be set, which significantly facilitates the Evolutionary Algorithm parameter tuning analysis. This study presents a comprehensive analysis of the commonly used non-parametric selection mechanisms. Comparison of the selection mechanisms is performed for the machine scheduling problem. The objective of the presented mathematical model is to determine the assignment of the arriving jobs among the available machines, and the processing order of jobs on each machine, aiming to minimize the total job processing cost. Different categories of Evolutionary Algorithms, which deploy various non-parametric selection mechanisms, are evaluated in terms of the objective function value at termination, computational time, and changes in the population diversity. Findings indicate that the Roulette Wheel Selection and Uniform Sampling selection mechanisms generally yield higher population diversity, while the Stochastic Universal Sampling selection mechanism outperforms the other non-parametric selection mechanisms in terms of the solution quality.","PeriodicalId":408183,"journal":{"name":"Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114446819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Robust Optimization: Concepts and Applications 鲁棒优化:概念和应用

Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization Pub Date : 2018-07-18 DOI: 10.5772/INTECHOPEN.75381

José Álvarez García, Alvaro Peña

引用次数: 33