Optimization heuristics in econometrics: applications of threshold accepting
Optimization heuristics in econometrics: applications of threshold accepting
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Journal of Global Optimization
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
Accelerating the Convergence of Evolutionary Algorithms by Fitness Landscape Approximation
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
A Knowledge-Based Approach To Response Surface Modelling in Multifidelity Optimization
Journal of Global Optimization
Kriging as a surrogate fitness landscape in evolutionary optimization
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models
Journal of Global Optimization
A genetic algorithm and a particle swarm optimizer hybridized with Nelder-Mead simplex search
Computers and Industrial Engineering - Special issue: Sustainability and globalization: Selected papers from the 32 nd ICC&IE
Widely convergent method for finding multiple solutions of simultaneous nonlinear equations
IBM Journal of Research and Development
Prediction of wireless network connectivity using a Taylor Kriging approach
International Journal of Advanced Intelligence Paradigms
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
A variable strength interaction test suites generation strategy using Particle Swarm Optimization
Journal of Systems and Software
Model-based control of natural ventilation in dairy buildings
Computers and Electronics in Agriculture
Hi-index | 0.00 |
This paper develops a simulation optimization algorithm based on Taylor Kriging and evolutionary algorithm (SOAKEA) for simulation models with high computational expenses. In SOAKEA, an evolutionary algorithm is used to search for optimal solutions of a simulation model, and Taylor Kriging temporarily serves as a surrogate fitness function of this evolutionary algorithm to evaluate solutions. Taylor Kriging is an enhanced version of Kriging where Taylor expansion is used to approximate the drift function of Kriging, and it improves the interpolation accuracy of Kriging. The structures and properties of SOAKEA are analyzed. A combination correction strategy is created, and it effectively reduces the computational expense of SOAKEA. The empirical comparison of SOAKEA with some other well-known metaheuristics is conducted, and the proposed SOAKEA uses particle swam optimization, a population-based evolutionary algorithm, to solve four simulation problems based on multimodal benchmark functions. The results indicate that SOAKEA has significant advantages in optimizing simulation models with high computational expenses.