The multi-objective optimization problem is difficult to solve with conventional methods and algorithms because there are conflicts among several objectives functions. Through the efforts of researchers experts from different fields for last 30 years, research application evolutionary (MOEA) have made excellent progress in solving such problems. MOEA has become one primary used technologies rea...

Abstract Hyperparameter optimization (HPO) is a necessary step to ensure the best possible performance of Machine Learning (ML) algorithms. Several methods have been developed perform HPO; most these are focused on optimizing one measure (usually an error-based measure), and literature such single-objective HPO problems vast. Recently, though, algorithms appeared that focus multiple conflicting...

In this paper, we proposed an algorithm for solving the problem of task scheduling using particle swarm optimization algorithm, with changes in the Selection and removing the guide and also using the technique to get away from the bad, to move away from local extreme and diversity. Scheduling algorithms play an important role in grid computing, parallel tasks Scheduling and sending them to ...

fuzzy clustering methods are conveniently employed in constructing a fuzzy model of a system, but they need to tune some parameters. in this research, fcm is chosen for fuzzy clustering. parameters such as the number of clusters and the value of fuzzifier significantly influence the extent of generalization of the fuzzy model. these two parameters require tuning to reduce the overfitting in the...

This paper introduces a novel multi-objective evolutionary algorithm based on cat swarm optimizationalgorithm (EMCSO) and its application to solve a multi-objective knapsack problem. The multi-objective optimizers try to find the closest solutions to true Pareto front (POF) where it will be achieved by finding the less-crowded non-dominated solutions. The proposed method applies cat swarm optim...