Multi-Objective Optimization Problems
Concepts and Self-Adaptive Parameters with Mathematical and Engineering Applications
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Beschreibung
This book is aimed at undergraduate and graduate students in applied mathematics or computer science, as a tool for solving real-world design problems. The present work covers fundamentals in multi-objective optimization and applications in mathematical and engineering system design using a new optimization strategy, namely the Self-Adaptive Multi-objective Optimization Differential Evolution (SA-MODE) algorithm. This strategy is proposed in order to reduce the number of evaluations of the objective function through dynamic update of canonical Differential Evolution parameters (population size, crossover probability and perturbation rate). The methodology is applied to solve mathematical functions considering test cases from the literature and various engineering systems design, such as cantilevered beam design, biochemical reactor, crystallization process, machine tool spindle design, rotary dryer design, among others.
Offers an starting point to key concepts related to multi-objective optimization problems Brings a rich variety of applications in Engineering and Mathematics Presents a new optimization strategy, the Self-Adpative Multi-objective Optimization Differential Evolution (SA-MODE) algorithm in which the parameters are dynamically updated during the evolutionary process Gives a broad perspective on different solving methods based on classical and modern approaches
Autor*in
Fran Sérgio Lobato
Themen in »Multi-Objective Optimization Problems«
Multi-objective optimization Differential evolution Self-adaptive parameters MOOP MOP Evolutionary algorithms Genetic Algorithms SA-MODE SBMAC