Efficient Approximation Algorithms for Multi-objective Constraint Optimization
نویسنده
چکیده
In this paper, we propose new depth-first heuristic search algorithms to approximate the set of Pareto optimal solutions in multi-objective constraint optimization. Our approach builds upon recent advances in multi-objective heuristic search over weighted AND/OR search spaces and uses an -dominance relation between cost vectors to significantly reduce the set of non-dominated solutions. Our empirical evaluation on various benchmarks demonstrates the power of our scheme which improves the resolution times dramatically over recent stateof-the-art competitive approaches.
منابع مشابه
An effective method based on the angular constraint to detect Pareto points in bi-criteria optimization problems
The most important issue in multi-objective optimization problems is to determine the Pareto points along the Pareto frontier. If the optimization problem involves multiple conflicting objectives, the results obtained from the Pareto-optimality will have the trade-off solutions that shaping the Pareto frontier. Each of these solutions lies at the boundary of the Pareto frontier, such that the i...
متن کاملOPTIMAL DESIGN OF TRUSS STRUCTURES BY IMPROVED MULTI-OBJECTIVE FIREFLY AND BAT ALGORITHMS
The main aim of the present paper is to propose efficient multi-objective optimization algorithms (MOOAs) to tackle truss structure optimization problems. The proposed meta-heuristic algorithms are based on the firefly algorithm (FA) and bat algorithm (BA), which have been recently developed for single-objective optimization. In order to produce a well distributed Pareto front, some improvement...
متن کاملScalarizing cost-effective multi-objective optimization algorithms made possible with kriging
Purpose – The purpose of this paper is threefold: to make explicitly clear the range of efficient multi-objective optimization algorithms which are available with kriging; to demonstrate a previously uninvestigated algorithm on an electromagnetic design problem; and to identify algorithms particularly worthy of investigation in this field. Design/methodology/approach – The paper concentrates ex...
متن کاملApproximate Pareto Optimal Solutions of Multi objective Optimal Control Problems by Evolutionary Algorithms
In this paper an approach based on evolutionary algorithms to find Pareto optimal pair of state and control for multi-objective optimal control problems (MOOCP)'s is introduced. In this approach, first a discretized form of the time-control space is considered and then, a piecewise linear control and a piecewise linear trajectory are obtained from the discretized time-control space using ...
متن کاملDistributed Multi-Criteria Coordination in Multi-Agent Systems
Distributed constraint optimization (DCOP) has emerged as a key technique for multiagent coordination. Unfortunately, while previous work in DCOP focuses on optimizing a single team objective, domains often require satisfying additional criteria. This paper provides a novel multi-criteria DCOP algorithm, based on two key ideas: (i) transforming multi-criteria problems via virtual variables to h...
متن کامل