Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Swarm intelligence
Computational Optimization and Applications
A Genetic Algorithm for the Multidimensional Knapsack Problem
Journal of Heuristics
On the Effectivity of Evolutionary Algorithms for the Multidimensional Knapsack Problem
AE '99 Selected Papers from the 4th European Conference on Artificial Evolution
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A review of particle swarm optimization. Part I: background and development
Natural Computing: an international journal
Natural Computing: an international journal
A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem
Computers and Operations Research
A Chaotic Neural Network Combined Heuristic Strategy for Multidimensional Knapsack Problem
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Kernel search: A general heuristic for the multi-dimensional knapsack problem
Computers and Operations Research
The Multidimensional Knapsack Problem: Structure and Algorithms
INFORMS Journal on Computing
A novel set-based particle swarm optimization method for discrete optimization problems
IEEE Transactions on Evolutionary Computation
A binary particle swarm optimization for continuum structural topology optimization
Applied Soft Computing
In search of the essential binary discrete particle swarm
Applied Soft Computing
Ant colony optimization for multiple knapsack problem and model bias
NAA'04 Proceedings of the Third international conference on Numerical Analysis and its Applications
Genetic algorithm based on the orthogonal design for multidimensional knapsack problems
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Apply the particle swarm optimization to the multidimensional knapsack problem
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
An approach to multimodal biomedical image registration utilizing particle swarm optimization
IEEE Transactions on Evolutionary Computation
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The Particle Swarm Optimization (PSO) algorithm is an innovative and promising optimization technique in evolutionary computation. The Essential Particle Swarm Optimization queen (EPSOq) is one of the recent discrete PSO versions that further simplifies the PSO principles and improves its optimization ability. Hybridization is a principle of combining two (or more) approaches in a wise way such that the resulting algorithm includes the positive features of both (or all) the algorithms. This paper proposes a new heuristic approach such that various features inspired from the Tabu Search are incorporated in the EPSOq algorithm in order to obtain another improved discrete PSO version. The implementation of this idea is identified with the acronym TEPSOq (Tabu Essential Particle Swarm Optimization queen). Experimentally, this approach is able to solve optimally large-scale strongly correlated 0---1 Multidimensional Knapsack Problem (MKP) instances. Computational results show that TEPSOq has outperforms not only the EPSOq, but also other existing PSO-based approaches and some other meta-heuristics in solving the 0---1 MKP. It was discovered also that this algorithm is able to locate solutions extremely close and even equal to the best known results available in the literature.