Multi-objective optimization methods are essential to resolve real-world problems as most involve several types of objects. Several multi-objective genetic algorithms have been proposed. Among them, SPEA2 and NSGA-II are the most successful. In the present study, two new mechanisms were added to SPEA2 to improve its searching ability a more effective crossover mechanism and an archive mechanism...

this paper uses integrated data envelopment analysis (dea) models to rank all extreme and non-extreme efficient decision making units (dmus) and then applies integrated dea ranking method as a criterion to modify genetic algorithm (ga) for finding pareto optimal solutions of a multi objective programming (mop) problem. the researchers have used ranking method as a shortcut way to modify ga to d...

This paper develops a first comparative study of multiobjective algorithms in Multiple Instance Learning (MIL) applications. These algorithms use grammar-guided genetic programming, a robust classification paradigm which is able to generate understandable rules that are adapted to work with the MIL framework. The algorithms obtained are based on the most widely used and compared multi-objective...

integrated production-distribution planning (pdp) is one of the most important approaches in supply chain networks. we consider a supply chain network (scn) to consist of multi suppliers, plants, distribution centers (dcs), and retailers. a bi-objective mixed integer linear programming model for integrating production-distribution designed here aim to simultaneously minimize total net costs in ...

The paper presents a generalization of the Strength Pareto Evolutionary Algorithm 2 (SPEA2) and its application in selected well-known twoand threeobjective optimization benchmark problems. The proposed solution is referred to as our SPEA3. The generalization consists in the exchange of the environmental selection procedure in SPEA2 for a new original algorithm which aims to determine the final...

This paper presents a new strategy that can solve the Grid task scheduling problem with multiple objectives (NP-Hard) in polynomial time using evolutionary algorithms. The results obtained by our proposed algorithm were compared and evaluated against the -constraints classic Multi-Objective Optimization method, which uses the deterministic algorithm of Branch and Bound to find the real Pareto f...

Abstract—This paper presents an application of Multi-objective optimization and evolutive algorithms in a ZigBee network to determine the optimal routing tree for transmission. In this research the interference among nearby networks and the number of hops were taken as objective functions. The multi-objetive evolutionary algorithm SPEA (Strength Pareto Evolutionary Algorithm is used for optimiz...