Evolutionary algorithms have become a popular way of solving demanding and expensive optimisation problems. These algorithms are composed of several control parameters that need to be set, for the procedure of searching for the optimum of an objective function to be successful. Parameter setting is a...
1996. "Model Reduction in Control Systems by Means of Global Structure Evolution and Local Parameter Learning". Evolutionary Algorithms in Engineering Applications, ed. D. Dasgupta and Z. Michalewicz, Springer Verlag.Y. Li, K. C. Tan, and M. Gong. Model reduction in control systems by means...
Due to their population-based nature, they are suitable for solving MOPs because, if properly manipulated, they can generate multiple Pareto-optimal solutions in a single run. Particularly, during the last decades, numbers of multiobjective evolutionary algorithms (MOEAs) have been proposed [2], [...
One popular technique of improving metaheuristic algorithms is to apply adaptive parameter controller to the algorithm process. The roles which control parameters play are varied depending on the algorithm they are in, nonetheless their values often have great impacts on the operation of metaheuristic al...
摘要: Discusses methods proposed for handling nonlinear programming constraints by evolutionary algorithms for numerical optimization problems. Difficulties connected with solving general nonlinear programming problem; Approaches that have emerged in the evolutionary computation community....
techniques. Optimization problems often don’t have an exact solution as it may be too time-consuming and computationally intensive to find an optimal solution. However, evolutionary algorithms are ideal in such situations as they can be used to find a near-optimal solution which is often ...
(PSO)38, have been leveraged to minimize discrepancies between experimental and simulated current data in PV models. Despite their effectiveness, these techniques could benefit from enhancements in computational time efficiency. The GA, one of the most commonly used evolutionary algorithms, has been ...
If evolutionary algorithms are implemented they will follow the same basic structure, with the regression function replaced with whatever evolutionary mechanism one wishes to use. Configuration options variables The variables (or parameters) to identify in the optimization. The variables are ginven as a...
We describe in this paper an approach for mathematical function optimization using fuzzy logic for parameter tuning combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs). The proposed method combines the advantages of PSO and GA to give us an improved FPSO+FGA hybrid method. Fuzz...
Stochastic Learning and Adaptive Control Introduction Metaheuristics inspired by nature, such as evolutionary algorithms (EAs), form a major class of modern optimization methods [1]. One of the reasons underlying their success is that they provide the flexibility needed to solve diverse engineering prob...