Advances In Learning Automata And Intelligent Optimization
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Advances in Learning Automata and Intelligent Optimization
Author: Javidan Kazemi Kordestani
language: en
Publisher: Springer Nature
Release Date: 2021-06-23
This book is devoted to the leading research in applying learning automaton (LA) and heuristics for solving benchmark and real-world optimization problems. The ever-increasing application of the LA as a promising reinforcement learning technique in artificial intelligence makes it necessary to provide scholars, scientists, and engineers with a practical discussion on LA solutions for optimization. The book starts with a brief introduction to LA models for optimization. Afterward, the research areas related to LA and optimization are addressed as bibliometric network analysis. Then, LA's application in behavior control in evolutionary computation, and memetic models of object migration automata and cellular learning automata for solving NP hard problems are considered. Next, an overview of multi-population methods for DOPs, LA's application in dynamic optimization problems (DOPs), and the function evaluation management in evolutionary multi-population for DOPs are discussed. Highlighted benefits • Presents the latest advances in learning automata-based optimization approaches. • Addresses the memetic models of learning automata for solving NP-hard problems. • Discusses the application of learning automata for behavior control in evolutionary computation in detail. • Gives the fundamental principles and analyses of the different concepts associated with multi-population methods for dynamic optimization problems.
Advances in Learning Automata and Intelligent Optimization
This book is devoted to the leading research in applying learning automaton (LA) and heuristics for solving benchmark and real-world optimization problems. The ever-increasing application of the LA as a promising reinforcement learning technique in artificial intelligence makes it necessary to provide scholars, scientists, and engineers with a practical discussion on LA solutions for optimization. The book starts with a brief introduction to LA models for optimization. Afterward, the research areas related to LA and optimization are addressed as bibliometric network analysis. Then, LA's application in behavior control in evolutionary computation, and memetic models of object migration automata and cellular learning automata for solving NP hard problems are considered. Next, an overview of multi-population methods for DOPs, LA's application in dynamic optimization problems (DOPs), and the function evaluation management in evolutionary multi-population for DOPs are discussed. Highlighted benefits • Presents the latest advances in learning automata-based optimization approaches. • Addresses the memetic models of learning automata for solving NP-hard problems. • Discusses the application of learning automata for behavior control in evolutionary computation in detail. • Gives the fundamental principles and analyses of the different concepts associated with multi-population methods for dynamic optimization problems. .
Optimization Algorithms
Author: Mykhaylo Andriychuk
language: en
Publisher: BoD – Books on Demand
Release Date: 2024-07-10
Optimization Algorithms - Classics and Last Advances is devoted to developing algorithm theory and exploring the use of different optimization algorithms for solving various problems in pure science, applied physics, and information technology. The book consists of two sections. The first focuses on developing abstract algorithms with subsequent applications to real-world optimization problems. It discusses optimization problems based on partial differential equations, canonical polyadic decomposition, variational approach, and ant colony optimization, which are discussed here. The second section presents problems related to optimization in information technologies. Chapters in this section address the utilization of optimization algorithms to solve problems of reducing computation time and computer memory, reducing kernel mechanism processing time in multimedia authoring tools, arranging access optimization for special applications, and minimizing resources for solving vehicle routing problems.