This study provides a comprehensive assessment of state-of-the-art evolutionary multiobjective optimization (EMO) tools’ relative effectiveness in calibrating hydrologic models. The relative computational efficiency, accuracy, and ease-of-use of the following EMO algorithms are tested: Epsilon Dominance Nondominated Sorted Genetic AlgorithmII (ε-NSGAII), the Multiobjective Shuffled Complex Evol...

Optimizing the database queries is one of hard research problems. Exhaustive search techniques like dynamic programming is suitable for queries with a few relations, but by increasing the number of relations in query, much use of memory and processing is needed, and the use of these methods is not suitable, so we have to use random and evolutionary methods. The use of evolutionary methods, beca...

In multi-objective evolutionary algorithms (MOEAs), the traditional fitness assignment strategy based on Pareto dominance is ineffective in sorting out the highquality solutions when the number of the objective is large. Recently, many scholars have used preference order (PO) ranking approach as an optimality criterion in the ranking stage of MOEAs. The experiment shows that the algorithms equi...

In this paper, we discuss the idea of incorporating preference information into evolutionary multi-objective optimization and propose a preference-based evolutionary approach that can be used as an integral part of an interactive algorithm. One algorithm is proposed in the paper. At each iteration, the decision maker is asked to give preference information in terms of his or her reference point...

In this research report, the author proposes two new evolutionary approaches to Multiobjective Optimization Problems (MOPs)— Dynamic Particle Swarm Optimization (DPSMO) and Dynamic Particle Swarm Evolutionary Algorithm (DPSEA). In DPSMO, instead of using genetic operators (e.g., crossover and mutation), the information sharing technique in Particle Swarm Optimization is applied to inform the en...

In this paper, we discuss the idea of incorporating preference information into evolutionary multiobjective optimization and propose a preference-based evolutionary approach that can be used as an integral part of an interactive algorithm. One algorithm is proposed in the paper. At each iteration, the decision maker is asked to give preference information in terms of her/his reference point con...

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 ...

In this paper, three evolutionary algorithms have been discussed for solving three-criteria optimization problem of finding a set of Pareto-optimal program module assignments. An adaptive evolutionary algorithm has been recommended for solving an established multiobjective optimization problem. Moreover, a multi-criterion genetic algorithm and an evolution strategy have been considered. Some nu...

Atmospheric pollutants mainly produced by thermal power plants compel to utilize green energy sources such as renewable and hydroelectric in a system. But due blinking behavior of very high rate outages, it has detrimental consequence on overall grid. Demand side management (DSM) programs decrease cost improve system security. This study proposes non-dominated sorting genetic algorithm-II (NSGA...