Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book was established after the 6th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications.

model predictive control --- bulbous bow --- improvement differential evolution algorithm --- evolutionary multi-objective optimization --- location routing problem --- flexible job shop scheduling problem --- basic differential evolution algorithm --- metric measure spaces --- NEAT --- genetic algorithm --- multiobjective optimization --- improved differential evolution algorithm --- performance indicator --- rubber --- averaged Hausdorff distance --- mixture experiments --- U-shaped assembly line balancing --- Genetic Programming --- Local Search --- driving events --- surrogate-based optimization --- single component constraints --- crop planning --- Pareto front --- numerical simulations --- shape morphing --- genetic programming --- economic crops --- local search and jump search --- model order reduction --- optimal solutions --- EvoSpace --- risky driving --- intelligent transportation systems --- optimal control --- IV-optimality criterion --- Bloat --- decision space diversity --- modify differential evolution algorithm --- power means --- driving scoring functions --- open-source framework --- evolutionary computation --- differential evolution algorithm --- vehicle routing problem --- multi-objective optimization

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Evolutionary algorithms and metaheuristics are widely used to provide efficient and effective approximate solutions to computationally hard optimization problems. With the widespread use of intelligent systems in recent years, evolutionary algorithms have been applied, beyond classical optimization problems, to AI system parameter optimization and the design of artificial neural networks and feature selection in machine learning systems. This volume will present recent results of applications of the most successful metaheuristics, from differential evolution and particle swarm optimization to artificial neural networks, loT allocation, and multi-objective optimization problems. It will also provide a broad view of the role and the potential of evolutionary algorithms as service components in Al systems.

Information technology industries --- multi-objective optimization problems --- particle swarm optimization (PSO) --- Gaussian mutation --- improved learning strategy --- big data --- interval concept lattice --- horizontal union --- sequence traversal --- evolutionary algorithms --- multi-objective optimization --- parameter puning --- parameter analysis --- particle swarm optimization --- differential evolution --- global continuous optimization --- wireless sensor networks --- task allocation --- stochastic optimization --- social network optimization --- memetic particle swarm optimization --- adaptive local search operator --- co-evolution --- PSO --- formal methods in evolutionary algorithms --- self-adaptive differential evolutionary algorithms --- constrained optimization --- ensemble of constraint handling techniques --- hybrid algorithms --- association rules --- mining algorithm --- vertical union --- neuroevolution --- neural networks --- n/a

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Evolutionary algorithms and metaheuristics are widely used to provide efficient and effective approximate solutions to computationally hard optimization problems. With the widespread use of intelligent systems in recent years, evolutionary algorithms have been applied, beyond classical optimization problems, to AI system parameter optimization and the design of artificial neural networks and feature selection in machine learning systems. This volume will present recent results of applications of the most successful metaheuristics, from differential evolution and particle swarm optimization to artificial neural networks, loT allocation, and multi-objective optimization problems. It will also provide a broad view of the role and the potential of evolutionary algorithms as service components in Al systems.

Information technology industries --- multi-objective optimization problems --- particle swarm optimization (PSO) --- Gaussian mutation --- improved learning strategy --- big data --- interval concept lattice --- horizontal union --- sequence traversal --- evolutionary algorithms --- multi-objective optimization --- parameter puning --- parameter analysis --- particle swarm optimization --- differential evolution --- global continuous optimization --- wireless sensor networks --- task allocation --- stochastic optimization --- social network optimization --- memetic particle swarm optimization --- adaptive local search operator --- co-evolution --- PSO --- formal methods in evolutionary algorithms --- self-adaptive differential evolutionary algorithms --- constrained optimization --- ensemble of constraint handling techniques --- hybrid algorithms --- association rules --- mining algorithm --- vertical union --- neuroevolution --- neural networks

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development.

supply chain optimization --- oil and gas supply chain --- maintenance scheduling --- operation planning --- energy --- order picking --- wave planning --- warehouses --- distribution centers --- mixed integer programming --- non-linear programming --- Hadi-Vencheh model --- multiple criteria ABC inventory classification --- nonlinear weighted product model --- building material distributors --- central composite design (CCD) --- Box–Behnken design (BBD) --- optimal cost --- customer service level --- forecasting --- order planning --- inventory management --- resource-constrained project scheduling problem --- discounted cash flow maximization --- milestones payments --- simulated annealing algorithm --- slotting --- storage strategies --- stackability --- SKU --- product family --- heuristic --- metaheuristics --- scheduling --- injection molding --- hospital catering --- production scheduling --- flexible job shop problem --- mathematical model --- genetic algorithm --- local search method --- iterated local search algorithm --- competitive hub location problem --- network design --- food systems --- rural development --- mathematical programming --- crow search --- process planning --- operation sequencing --- precedence constraints --- manufacturing scheduling --- smart manufacturing --- intelligent manufacturing systems --- scheduling requirements --- cyber-physical production systems --- postman delivery --- vehicle routing problem --- particle swarm optimization algorithm --- differential evolution algorithm --- multi-criteria optimization --- simulation optimization --- production control --- multiple flexible job shop scheduling --- priority rules --- smart health care systems --- planning --- logistic systems --- benchmark --- workload balancing --- identical parallel machines --- normalized sum of square for workload deviations --- maximum completion time --- minimum completion time --- terminal location --- intermodal transportation --- simulated annealing --- mixed integer program --- incomplete networks --- n/a --- Box-Behnken design (BBD)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Computational intelligence is a general term for a class of algorithms designed by nature's wisdom and human intelligence. Computer scientists have proposed many computational intelligence algorithms with heuristic features. These algorithms either mimic the evolutionary processes of the biological world, mimic the physiological structure and bodily functions of the organism,

individual updating strategy --- integrated design --- global optimum --- flexible job shop scheduling problem --- whale optimization algorithm --- EHO --- bat algorithm with multiple strategy coupling (mixBA) --- multi-objective DV-Hop localization algorithm --- optimization --- rock types --- variable neighborhood search --- biology --- average iteration times --- CEC2013 benchmarks --- slicing tree structure --- firefly algorithm (FA) --- benchmark --- single loop --- evolutionary computation --- memetic algorithm --- normal cloud model --- 0-1 knapsack problems --- elite strategy --- diversity maintenance --- material handling path --- artificial bee colony algorithm (ABC) --- urban design --- entropy --- evolutionary algorithms (EAs) --- monarch butterfly optimization --- numerical simulation --- architecture --- set-union knapsack problem --- Wilcoxon test --- convolutional neural network --- global position updating operator --- particle swarm optimization --- computation --- minimum load coloring --- topology structure --- adaptive multi-swarm --- minimum total dominating set --- mutation operation --- shape grammar --- greedy optimization algorithm --- ?-Hilbert space --- genetic algorithm --- large scale optimization --- large-scale optimization --- NSGA-II-DV-Hop --- constrained optimization problems (COPs) --- first-arrival picking --- transfer function --- SPEA 2 --- stochastic ranking (SR) --- wireless sensor networks (WSNs) --- acceleration search --- convergence point --- fuzzy c-means --- evolutionary algorithm --- success rates --- Artificial bee colony --- particle swarm optimizer --- random weight --- range detection --- adaptive weight --- large-scale --- automatic identification --- cloud model --- swarm intelligence --- evolutionary multi-objective optimization --- DV-Hop algorithm --- bat algorithm (BA) --- Friedman test --- quantum uncertainty property --- facility layout design --- local search --- deep learning --- Y conditional cloud generator --- benchmark functions --- discrete algorithm --- dispatching rule --- DE algorithm --- nonlinear convergence factor --- energy-efficient job shop scheduling --- t-test --- evolution --- dimension learning --- global optimization --- confidence term --- elephant herding optimization --- moth search algorithm --- evolutionary