2022年7月4日 · Realistic problems typically have many conflicting objectives. Therefore, it is instinctive to look at the engineering problems as multi-objective optimization problems. This paper briefly …

2011年3月1日 · A multiobjective optimization problem involves several conflicting objectives and has a set of Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary …

2021年10月21日 · The optimization problems that must meet more than one objective are called multi-objective optimization problems and may present several optimal solutions. This manuscript brings …

2023年6月17日 · In the last two decades, the fast and elitist non-dominated sorting genetic algorithm (NSGA-II) has attracted extensive research interests, and it is still one of the hottest research …

Evolutionary algorithms are one such generic stochastic approach that has proven to be successful and widely applicable in solving both single-objective and multi-objective problems. This textbook is a …

Google Scholar Zitzler, E. and Thiele, L., 1998, An evolutionary algorithm for multi-objective optimization: The strength Pareto approach, Technical Report No. 43, Computer Engineering and …

2018年5月31日 · Finally, it highlights recent important trends and closely related research fields. The tutorial is intended for readers, who want to acquire basic knowledge on the mathematical …

2022年12月1日 · There are various constraints in many real engineering problems, and the constrained handling methods play a key role in the constrained multi-objective evolutionary algorithms …

3.2.1 Non-dominated sorting genetic algorithm-II Non-dominated sorting genetic algorithm II (NSGA-II) is an efficient meta-heuristic multi-objective genetic algorithm that is commonly deployed to solve multi …

Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is a multi-objective evolutionary algorithm that reduces computational complexity, eliminates the need for specifying a sharing parameter, and …