Evolutionary Computation
Alavi, Amir
Evolutionary Computation - MDPI - Multidisciplinary Digital Publishing Institute 2019 - 1 electronic resource (424 p.)
Open Access
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,
Creative Commons
English
books978-3-03921-929-2 9783039219285 9783039219292
10.3390/books978-3-03921-929-2 doi
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
Evolutionary Computation - MDPI - Multidisciplinary Digital Publishing Institute 2019 - 1 electronic resource (424 p.)
Open Access
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,
Creative Commons
English
books978-3-03921-929-2 9783039219285 9783039219292
10.3390/books978-3-03921-929-2 doi
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