05391nam 2201369z- 450 991055769920332120220111(CKB)5400000000044565(oapen)https://directory.doabooks.org/handle/20.500.12854/77036(oapen)doab77036(EXLCZ)99540000000004456520202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierEvolutionary Computation 2020Basel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online resource (442 p.)3-0365-2394-4 3-0365-2395-2 Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms.Technology: general issuesbicssc0-1 knapsack problemant colony optimizationassortative matingbinary whale optimization algorithmbug detectionbWOA-SbWOA-Vcitationclassificationcoevolutionconstrained optimizationcuckoo search algorithmdecomposition-based multi-objective optimisationdifferential evolutiondimensionality reductiondiscrete artificial bee colony algorithmdiversity preservationdominancedynamic learningelephant herding optimizationengineering optimizationevolutionary algorithmevolutionary algorithms (EAs)evolutionary computationfeature selectionfuzzingfuzzy hybrid flow shop schedulinggame featuregame simulationgame treesgeoelectric modelglobal optimizationgreen shop schedulinggrey wolf optimizerh-indexiterated local searchknapsack problemknowledge transferkrill herdmagnetotelluricmany-objective optimizationmemetic algorithmmenu planning problemmetaheuristicminimize makespanminimize total energy consumptionmulti-indicatorsmulti-metricmulti-objective optimizationmulti-resourcesmulti-task evolutionary computationmulti-task optimizationmutationone-dimensional inversionsopposite pathopposition-based learningoptimization problemPareto optimalityPareto-frontparticle swarm optimizationpath discoveryperformance indicatorsplaytestingplaytesting metricpremature convergenceQ-learningquantumquantum computingrankingseed scheduleself-adaptive step sizesimulated annealingsingle objective optimizationsingle-objective optimizationsuccess-historyswarm intelligencetraveling salesman problemstravelling salesman problemturning-based mutationunified search spaceuniversities rankingvalidationwhale optimization algorithmWOATechnology: general issuesWang Gai-Geedt1322388Alavi AmiredtWang Gai-GeothAlavi AmirothBOOK9910557699203321Evolutionary Computation 20203034943UNINA