Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Panel: simulation optimization: future of simulation optimization
Proceedings of the 33nd conference on Winter simulation
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
New advances and applications for marrying simulation and optimization
WSC '04 Proceedings of the 36th conference on Winter simulation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Nature-Inspired Metaheuristic Algorithms: Second Edition
Nature-Inspired Metaheuristic Algorithms: Second Edition
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
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This paper describes a case study of real-world simulation-based optimization of an engine manufacturing line. The optimization aims to maximize utilization of machines and at the same time minimize tied-up capital by manipulating 56 unique decision variables. A recently proposed metaheuristic algorithm that has achieved successful results in various problem domains called Cuckoo Search is used to perform the simulation-based optimization. To handle multiple objectives, an extension of the original Cuckoo Search algorithm based on the concept of Pareto optimality is proposed and used in the study. The performance of the algorithm is analyzed in comparison with the benchmark algorithm NSGA-II. Results show that the combinatorial nature of the optimization problem causes difficulties for the Cuckoo Search algorithm, and a further analysis indicates that the algorithm might be best suited for continuous optimization problems.