Multi-objective evolutionary computation and fuzzy 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...

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

A NOVEL FUZZY MULTI-OBJECTIVE ENHANCED TIME EVOLUTIONARY OPTIMIZATION FOR SPACE STRUCTURES

This research presents a novel design approach to achieve an optimal structure established upon multiple objective functions by simultaneous utilization of the Enhanced Time Evolutionary Optimization method and Fuzzy Logic (FLETEO). For this purpose, at first, modeling of the structure design problem in this space is performed using fuzzy logic concepts. Thus, a new problem creates with functio...

Evolutionary Multi-Objective Robust Optimization

Multi-Objective Benchmark Studies for Evolutionary Computation

During the past few decades, many global optimisation and multi-objective evolutionary algorithms (MOEAs) have been developed. Those algorithms have shown very useful in enabling system design automation and globally accurate modelling. However, there is a lack of systematic benchmark measures that may be used to assess the merit and performance of these algorithms [1],[2],[3],[7],[8]. Such ben...