Open AccessBook
Multiple Criteria Optimization: Theory, Computation, and Application
Reads0
Chats0
TLDR
Mathematical Background Topics from Linear Algebra Single Objective Linear Programming Determining all Alternative Optima Comments about Objective Row Parametric Programming Utility Functions, Nondominated Criterion Vectors and Efficient Points Point Estimate Weighted-sums Approach.Abstract:
Mathematical Background Topics from Linear Algebra Single Objective Linear Programming Determining all Alternative Optima Comments about Objective Row Parametric Programming Utility Functions, Nondominated Criterion Vectors and Efficient Points Point Estimate Weighted-sums Approach Optimal Weighting Vectors, Scaling and Reduced Feasible Region Methods Vector-Maximum Algorithms Goal Programming Filtering and Set Discretization Multiple Objective Linear Fractional Programming Interactive Procedures Interactive Weighted Tchebycheff Procedure Tchebycheff/Weighted-Sums Implementation Applications Future Directions Index.read more
Citations
More filters
Book ChapterDOI
Concave Minimization: Theory, Applications and Algorithms
TL;DR: The purpose of this chapter is to present the essential elements of the theory, applications, and solution algorithms of concave minimization, including three fundamental classes of solution approaches that use deterministic (rather than stochastic) methods.
Journal ArticleDOI
Interactive multiobjective optimization system WWW-NIMBUS on the internet
Kaisa Miettinen,Marko M. Mäkelä +1 more
TL;DR: The NIMBUS algorithm and its implementation WWW-NIMBUS is described, which is the first interactive multiobjective optimization system on the Internet, and the main principles of its implementation are centralized computing and a distributed interface.
Journal ArticleDOI
Improved sampling of the Pareto-front in multiobjective genetic optimizations by steady-state evolution: a Pareto converging genetic algorithm
Rajeev Kumar,Peter I. Rockett +1 more
TL;DR: A simple steady-state strategy, Pareto Converging Genetic Algorithm (PCGA), which naturally samples the solution space and ensures population advancement towards the Pare to-front and eliminates the need for sharing/niching and thus minimizes heuristically chosen parameters and procedures.
Journal ArticleDOI
Multi-attribute group decision making model under the condition of uncertain information
TL;DR: The modification and extension of TOPSIS to a group decision environment is investigated in this article, where the authors adopt the Minkowski distance function to solve the over-weighted problem in the original top-SIS technique, the grey number operations to deal with the problem of uncertain information, and the aggregation approach to integrate experts' evaluations.
Book ChapterDOI
Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization
TL;DR: This chapter introduces two Perl programs that implement graphical tools for exploring the performance of stochastic local search algorithms for biobjective optimization problems based on the concept of the empirical attainment function (EAF), which describes the probabilistic distribution of the outcomes obtained by a Stochastic algorithm in the objective space.