Multiple objective genetic algorithms (MOGA) are heuristics that have seen relatively little use in geographic information science and spatial decision support in comparison to other geocomputational methods, despite their potential to generate robust alternative options for decision making. MOGA based decision analysis relieves the burden of a priori preference specification by an analyst, but...

This paper describes the application and performance evaluation of a new algorithm for multiple objective optimization problems (MOOP) based on reinforcement learning. The new algorithm, called MDQL, considers a family of agents for each objective function involved in a MOOP. Each agent proposes a solution for its corresponding objective function. Agents leave traces while they construct soluti...

An interactive fuzzy multiple objective optimization method was proposed for solving fuzzy multiple objective linear programming problems where fuzzy parameters in both objective functions and constraints and fuzzy goals of objectives can be in any form of membership function. Based on the method, in this paper, a fuzzy multiple objective decision support system prototype is developed. A detail...

In the classical problems of mathematical programming the coefficients of the problems are assumed to be exactly known. This assumption is seldom satisfied by the great majority of real-life problems. Taking into account the multiple criteria optimization in the environment of fuzzy constraints and mixed variables, a solving method is developed here.

An important problem in computer vision is the determination of weights for multiple objective function optimization. This problem arises naturally in many reconstruction problems, where one wishes to reconstruct a function belonging to a constrained class of signals based upon noisy observed data. A common approach is to combine the objective functions into a single total cost function. The pr...

Three rejection tests for multi-objective optimization problems based on first order optimality conditions are proposed. These tests can certify that a box does not contain any local minimizer, and thus it can be excluded from the search process. They generalize previously proposed rejection tests in several regards: Their scope include inequality and equality constrained smooth or nonsmooth mu...

This paper presents a multiple reference point approach for multi-objective optimization problems of discrete and combinatorial nature. When approximating the Pareto Frontier, multiple reference points can be used instead of traditional techniques. These multiple reference points can easily be implemented in a parallel algorithmic framework. The reference points can be uniformly distributed wit...

For some decades radiation therapy has been proved successful in cancer treatment. It is the major task of clinical radiation treatment planning to realise on the one hand a high level dose of radiation in the cancer tissue in order to obtain maximum tumour control. On the other hand it is obvious that it is absolutely necessary to keep in the tissue outside the tumour, particularly in organs a...

In this paper, experimentally derived correlations of heat transfer and pressure drop are used in a Pareto based Multi-Objective Optimization (MOO) approach to find the best possible combinations of heat transfer and pressure drop of TiO2-water nanofluid flow in tubes fitted with multiple twisted tape inserts in different arrangement. In this study there are four independent design variables: t...