A new multiobjective simulated annealing algorithm

Date

2007-09-01

Author

Tekinalp, Ozan

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

265
views

0
downloads

A new multiobjective simulated annealing algorithm for continuous optimization problems is presented. The algorithm has an adaptive cooling schedule and uses a population of fitness functions to accurately generate the Pareto front. Whenever an improvement with a fitness function is encountered, the trial point is accepted, and the temperature parameters associated with the improving fitness functions are cooled. Beside well known linear fitness functions, special elliptic and ellipsoidal fitness functions, suitable for the generation on non-convex fronts, are presented. The effectiveness of the algorithm is shown through five test problems. The parametric study presented shows that more fitness functions as well as more iteration gives more non-dominated points closer to the actual front. The study also compares the linear and elliptic fitness functions. The success of the algorithm is also demonstrated by comparing the quality metrics obtained to those obtained for a well-known evolutionary multiobjective algorithm.

Subject Keywords

Management Science and Operations Research, Control and Optimization, Applied Mathematics, Computer Science Applications

URI

https://hdl.handle.net/11511/37879

Journal

JOURNAL OF GLOBAL OPTIMIZATION

DOI

https://doi.org/10.1007/s10898-006-9120-2

Collections

Department of Aerospace Engineering, Article

Suggestions

OpenMETU
Core

The semismooth approach for semi-infinite programming under the Reduction Ansatz Stein, Oliver; Tezel, Aysun (Springer Science and Business Media LLC, 2008-06-01) We study convergence of a semismooth Newton method for generalized semi-infinite programming problems with convex lower level problems where, using NCP functions, the upper and lower level Karush-Kuhn-Tucker conditions of the optimization problem are reformulated as a semismooth system of equations. Nonsmoothness is caused by a possible violation of strict complementarity slackness. We show that the standard regularity condition for convergence of the semismooth Newton method is satisfied under natural assu...
Effective optimization with weighted automata on decomposable trees Ravve, E. V.; Volkovich, Z.; Weber, Gerhard Wilhelm (Informa UK Limited, 2014-01-02) In this paper, we consider quantitative optimization problems on decomposable discrete systems. We restrict ourselves to labeled trees as the description of the systems and we use weighted automata on them as our computational model. We introduce a new kind of labeled decomposable trees, sum-like weighted labeled trees, and propose a method, which allows us to reduce the solution of an optimization problem, defined in a fragment of Weighted Monadic Second Order Logic, on such a tree to the solution of effec...
An interactive algorithm for multiobjective ranking for underlying linear and quasiconcave value functions TEZCANER ÖZTÜRK, DİCLEHAN; Köksalan, Mustafa Murat (Wiley, 2019-07-29) We develop interactive algorithms to find a strict total order for a set of discrete alternatives for two different value functions: linear and quasiconcave. The algorithms first construct a preference matrix and then find a strict total order. Based on the ordering, they select a meaningful pair of alternatives to present the decision maker (DM) for comparison. We employ methods to find all implied preferences of the DM, after he or she makes a preference. Considering all the preferences of the DM, the pre...
A new likelihood approach to autonomous multiple model estimation Söken, Halil Ersin (Elsevier BV, 2020-04-01) This paper presents an autonomous multiple model (AMM) estimation algorithm for hybrid systems with sudden changes in their parameters. Estimates of Kalman filters (KFs) that are tuned and employed for different system modes are merged based on a newly defined likelihood function without any necessity for filter interaction. The proposed likelihood function is composed of two measures, the filter agility measure and the steady-state error measure. These measures are derived based on filter adaptation rules....
Discrete size optimization of steel trusses using a refined big bang-big crunch algorithm Hasançebi, Oğuzhan (Informa UK Limited, 2014-01-02) This article presents a methodology that provides a method for design optimization of steel truss structures based on a refined big bang-big crunch (BB-BC) algorithm. It is shown that a standard formulation of the BB-BC algorithm occasionally falls short of producing acceptable solutions to problems from discrete size optimum design of steel trusses. A reformulation of the algorithm is proposed and implemented for design optimization of various discrete truss structures according to American Institute of St...

Citation Formats

O. Tekinalp, “A new multiobjective simulated annealing algorithm,” JOURNAL OF GLOBAL OPTIMIZATION, pp. 49–77, 2007, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/37879.