Multiobjective evolutionary algorithm test suites
Proceedings of the 1999 ACM symposium on Applied computing
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Computers and Operations Research
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Bi-Objective Ant Colony Optimization approach to optimize production and maintenance scheduling
Computers and Operations Research
Expert Systems with Applications: An International Journal
DEM: a discrete electromagnetism-like mechanism for solving discrete problems
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
International Journal of Bio-Inspired Computation
Very large-scale neighborhood search for solving multiobjective combinatorial optimization problems
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
A heuristic approach for allocation of data to RFID tags: A data allocation knapsack problem (DAKP)
Computers and Operations Research
Novel binary biogeography-based optimization algorithm for the knapsack problem
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
Hi-index | 0.01 |
This paper presents a new multiobjective genetic algorithm based on the Tchebycheff scalarizing function, which aims to generate a good approximation of the nondominated solution set of the multiobjective problem. The algorithm performs several stages, each one intended for searching potentially nondominated solutions in a different part of the Pareto front. Pre-defined weight vectors act as pivots to define the weighted-Tchebycheff scalarizing functions used in each stage. Therefore, each stage focuses the search on a specific region, leading to an iterative approximation of the entire nondominated set. This algorithm, called MOTGA (Multiple objective Tchebycheff based Genetic Algorithm) has been designed to the multiobjective multidimensional 0/1 knapsack problem, for which a dedicated routine to repair infeasible solutions was implemented. Computational results are presented and compared with the outcomes of other evolutionary algorithms.