300 research outputs found
Finding and Exploring Promising Search Space for the 0-1 Multidimensional Knapsack Problem
The 0-1 multidimensional knapsack problem(MKP) is a classical NP-hard
combinatorial optimization problem. In this paper, we propose a novel heuristic
algorithm simulating evolutionary computation and large neighbourhood search
for the MKP. It maintains a set of solutions and abstracts information from the
solution set to generate good partial assignments. To find high-quality
solutions, integer programming is employed to explore the promising search
space specified by the good partial assignments. Extensive experimentation with
commonly used benchmark sets shows that our approach outperforms the state of
the art heuristic algorithms, TPTEA and DQPSO, in solution quality. It finds
new lower bound for 8 large and hard instance
A simplified binary artificial fish swarm algorithm for 0–1 quadratic knapsack problems
Available online 8 October 2013.This paper proposes a simplified binary version of the artificial fish swarm
algorithm (S-bAFSA) for solving 0–1 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 local search is applied on a predefined number of points. The
method is tested on a set of benchmark 0–1 knapsack problems.Fundação para a Ciência e a Tecnologia (FCT
Evolutionary Computation 2020
Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms
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