Paper Info
Title
Guided Pareto Local Search based frameworks for biobjective optimization
Abstract
Guided Pareto Local Search (GPLS) is an extension to the Guided Local Search algorithm to contain multiobjective combinatorial optimization. GPLS is shown to improve the convergence of the underlying Pareto local search algorithms. This paper demonstrates the potential of GPLS to be an effective searching technique that can be a central part of a multi-phase or hybrid frameworks. To confirm this, two simple frameworks based on GPLS are proposed: iGPLS and mGPLS. Both frameworks only require an initial set of diverse solutions. While GPLS starts from a randomly (or heuristically) generated solution, iGPLS starts with the initial diverse solution set. On the other hand, mGPLS is a parallel version of GPLS, in which each GPLS run starts independently from a solution in the initial set. The application of these frameworks to the biobjective 0/1 knapsack problem reveals the effectiveness of the GPLS based frameworks, demonstrated by achieving state-of-the-art results.
Year
DOI
Venue
2010
10.1109/CEC.2010.5585983
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
multiobjective combinatorial optimization,combinatorial mathematics,biobjective optimization,biobjective 0/1 knapsack problem,search problems,convergence,pareto optimisation,guided pareto local search,searching technique,knapsack problems,local search,maintenance engineering,optimization,guided local search,generators,evolutionary computation,knapsack problem,approximation algorithms,local search algorithm,combinatorial optimization
Convergence (routing),Approximation algorithm,Heuristic,Mathematical optimization,Guided Local Search,Computer science,Evolutionary computation,Combinatorial optimization,Artificial intelligence,Solution set,Knapsack problem,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4244-6909-3
12
0.56
References 
Authors
7
2
Name
Order
Citations
PageRank
Abdullah Alsheddy1373.99
Edward P. K. Tsang289987.77