Evolutionary Computation and Optimization

Multi-objective evolutionary computation and fuzzy optimization

In fuzzy optimization it is desirable that all fuzzy solutions under consideration be attainable, so that the decision maker will be able to make ‘‘a posteriori’’ decisions according to current decision environments. No additional optimization runs will be needed when the decision environment changes or when the decision maker needs to evaluate several decisions to establish the most appropriat...

Evolutionary computation for modeling and optimization

Modeling and Simulation Optimization Using Evolutionary Computation

Evolutionary computation (EC) is a general term applied to a group of global optimization techniques whose main characteristics are inspired by biological evolution. Instead of working with one possible solution at a time, they usually start with a population of random solutions. The initial population evolves into a better set of solutions through three main processes: selection, recombination...

Multi-Objective Optimization using Evolutionary Computation Techniques

In this paper an EP and PSO based optimization algorithms have been proposed for solving optimal power flow problems with multiple objective functions. These algorithms take into consideration all the equality and inequality constraints. The improvement in system performance is based on reduction in cost of power generation and active power loss. The

Hybrid Evolutionary Computation for Continuous Optimization

Hybrid optimization algorithms have gained popularity as it has become apparent there cannot be a universal optimization strategy which is globally more beneficial than any other. Despite their popularity, hybridization frameworks require more detailed categorization regarding: the nature of the problem domain, the constituent algorithms, the coupling schema and the intended area of application...