A modified nature inspired meta-heuristic whale optimization algorithm for solving 0–1 knapsack problem

The Whale optimization algorithm (WOA) is one of the most recent nature-inspired meta-heuristic optimization algorithms, which simulates the social character of the humpback whales. WOA has an efficient performance in solving the continuous problems and engineering optimization problems. This paper...

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Bibliographic Details
Published in:International journal of machine learning and cybernetics 2019-03, Vol.10 (3), p.495-514
Main Authors: Abdel-Basset, Mohamed, El-Shahat, Doaa, Sangaiah, Arun Kumar
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Complex Systems
Computational Intelligence
Control
Efficiency
Engineering
Heuristic
Heuristic methods
Knapsack problem
Mechatronics
Operators (mathematics)
Optimization
Optimization algorithms
Original Article
Pattern Recognition
Penalty function
Production planning
Quantum computing
Robotics
Robustness (mathematics)
Source code
Systems Biology
Whales & whaling
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Summary:The Whale optimization algorithm (WOA) is one of the most recent nature-inspired meta-heuristic optimization algorithms, which simulates the social character of the humpback whales. WOA has an efficient performance in solving the continuous problems and engineering optimization problems. This paper presents an improved whale optimization algorithm (IWOA) for solving both single and multidimensional 0–1 knapsack problems with different scales. A penalty function is added to the evaluation function so that the fitness of the feasible solutions can outperform the fitness of the infeasible ones. The sigmoid function can take the real-valued solutions as input and produces the binary solutions as output. A two-stage repair operator is employed for handling the infeasible solutions. IWOA can give a better tradeoff between the diversification and the intensification through using two strategies: Local Search Strategy (LSS) and the Lévy flight walks. In addition to the bitwise operation which increases the IWOA efficiency. The proposed IWOA is compared with other state-of-art algorithms to validate the effectiveness of the proposed algorithm in solving 0–1 knapsack problems. Further, the experimental results and extensive numerical illustrations have been indicated that IWOA is efficient, effective, and robust for solving the hard 0–1 knapsack problems than the other existing approaches in the available literature. The source code of the IWOA algorithm will be available after the paper accepted as a toolbox in MATLAB library.
ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-017-0731-3