Maintaining an archive of all non-dominated points is a standard task in multi-objective optimization. A simple solution is to store all evaluated points and to obtain the non-dominated subset in a post-processing step. Alternatively the non-dominated set can be updated on the fly. Surprisingly, there exists little work on the latter setting. While keeping track of many non-dominated points eff...

in this paper, a multi-objective reconfiguration problem has been solved simultaneously by a modified ant colony optimization algorithm. two objective functions, real power loss and energy not supplied index (ens), were utilized. multi-objective modified ant colony optimization algorithm has been generated by adding non-dominated sorting technique and changing the pheromone updating rule of ori...

This paper discusses a new hybrid solution method for a bi-objective routing problem, namely the bi-objective ring star problem. The bi-objective ring star problem is a generalization of the ring star problem in which the assignment cost has been dissociated from the cost of visiting a subset of nodes. Here, we investigate the possible contribution of incorporating specialized TSP heuristics in...

In decision analysis, difficulties of obtaining complete information about model parameters make it advisable to seek robust solutions that perform reasonably well across the full range of feasible parameter values. In this paper, we develop the Robust Portfolio Modeling (RPM) methodology which extends Preference Programming methods into portfolio problems where a subset of project proposals ar...

We compare the efficiency of the NSGA-II algorithm for the brachytherapy dose optimization problem with and without supporting solutions. A local search method enhances the efficiency of the algorithm. In comparison to a fast simulated annealing algorithm the supported hybrid NSGA-II algorithm provides much faster many non-dominated solutions. An archiving of all non-dominated solutions is usef...

This paper compares ant colony optimization (ACO) and evolutionary multi-objective optimization (EMO) for the weather avoidance in a free flight environment. The problem involves a number of potentially conflicting objectives such as minimizing deviations, weather avoidance, minimizing distance traveled and hard constraints like aircraft performance. Therefore, we modeled the problem as a multi...

Convex hull; Non-dominated solutions; Multi-objective evolutionary algorithms; Complexity; Ranking procedure. Abstract Due tomany applications of multi-objective evolutionary algorithms in real world optimization problems, several studies have been done to improve these algorithms in recent years. Since most multi-objective evolutionary algorithms are based on the non-dominated principle, and t...

This chapter presents three MS Excel programs, namely, EMOO (Excel based Multi-Objective Optimization), NDS (Non-Dominated Sorting) and PM (Performance Metrics) useful for Multi-Objective Optimization (MOO) studies. The EMOO program is for finding non-dominated solutions of a given MOO problem. It has both binary-coded and realcoded NSGA-II (Elitist Non-Dominated Sorting Genetic Algorithm), and...

Distribution centers (DCs) play important role in maintaining the uninterrupted flow of goods and materials between the manufacturers and their customers.This paper proposes a mathematical model as the bi-objective capacitated multi-vehicle allocation of customers to distribution centers. An evolutionary algorithm named non-dominated sorting ant colony optimization (NSACO) is used as the optimi...