Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Solving quadratic (0,1)-problems by semidefinite programs and cutting planes
Mathematical Programming: Series A and B
Exact Solution of the Quadratic Knapsack Problem
INFORMS Journal on Computing
Using a Mixed Integer Programming Tool for Solving the 0-1 Quadratic Knapsack Problem
INFORMS Journal on Computing
Greedy, genetic, and greedy genetic algorithms for the quadratic knapsack problem
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
The quadratic knapsack problem-a survey
Discrete Applied Mathematics
Solution of Large Quadratic Knapsack Problems Through Aggressive Reduction
INFORMS Journal on Computing
A novel quantum evolutionary algorithm for quadratic knapsack problem
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Review Article: Solving 0-1 knapsack problem by a novel global harmony search algorithm
Applied Soft Computing
Reoptimization in Lagrangian methods for the 0-1 quadratic knapsack problem
Computers and Operations Research
An augmented Lagrangian fish swarm based method for global optimization
Journal of Computational and Applied Mathematics
Novel fish swarm heuristics for bound constrained global optimization problems
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part III
DisABC: A new artificial bee colony algorithm for binary optimization
Applied Soft Computing
An artificial fish swarm algorithm based and ABC supported qos unicast routing scheme in NGI
ISPA'06 Proceedings of the 2006 international conference on Frontiers of High Performance Computing and Networking
Solving multidimensional 0---1 knapsack problem with an artificial fish swarm algorithm
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
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This paper proposes a simplified binary version of the artificial fish swarm algorithm (S-bAFSA) for solving 0-1 quadratic knapsack problems. This is a combinatorial optimization problem, which arises in many fields of optimization. In S-bAFSA, trial points are created by using crossover and mutation. In order to make the points feasible, a random heuristic drop_item procedure is used. The heuristic add_item is also implemented to improve the quality of the solutions, and a cyclic reinitialization of the population is carried out to avoid convergence to non-optimal solutions. To enhance the accuracy of the solution, a swap move heuristic search is applied on a predefined number of points. The method is tested on a set of benchmark 0-1 knapsack problems.