Binary Moth Search Algorithm for Discounted {0-1} Knapsack Problem

The discounted {0-1} knapsack problem (DKP) extends the classical 0-1 knapsack problem (0-1 KP) in which a set of item groups is included and each group consists of three items, whereas at most one of the three items can be packed into the knapsack. Therefore, the DKP is more complicated and computationally difficult than 0-1 KP. The DKP has been found many applications in real economic problems and other areas. In this paper, the influence of Lévy flights operator and fly straightly operator in the moth search (MS) algorithm is verified. Nine types of new mutation operator based on the global harmony search are specially devised to replace Lévy flights operator. Then, nine novel MS-based algorithms for DKP are proposed (denoted by MS1–MS9). Extensive experiments on three sets of 30 DKP instances demonstrate the remarkable performance of the proposed nine new MS-based approaches. In particular, it discovers that MS1–MS3 show better comprehensive performance among 10 algorithms. A variety of analyses indicate the important contribution of the individual of memory consideration in MS1–MS9.

[1]  Mahamed G. H. Omran,et al.  Global-best harmony search , 2008, Appl. Math. Comput..

[2]  Zhihua Cui,et al.  Monarch butterfly optimization , 2015, Neural Computing and Applications.

[3]  Suash Deb,et al.  Solving 0–1 knapsack problem by a novel binary monarch butterfly optimization , 2017, Neural Computing and Applications.

[4]  S. Deb,et al.  Elephant Herding Optimization , 2015, 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI).

[5]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[6]  Li Wenbin,et al.  Research on Genetic Algorithms for the Discounted{0-1}Knapsack Problem , 2016 .

[7]  Dan Simon,et al.  Biogeography-Based Optimization , 2022 .

[8]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[9]  Xin-She Yang,et al.  Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.

[10]  Amir Hossein Gandomi,et al.  Chaotic Krill Herd algorithm , 2014, Inf. Sci..

[11]  Panos M. Pardalos,et al.  A human learning optimization algorithm and its application to multi-dimensional knapsack problems , 2015, Appl. Soft Comput..

[12]  Amir Hossein Gandomi,et al.  Stud krill herd algorithm , 2014, Neurocomputing.

[13]  Xiangtao Li,et al.  A perturb biogeography based optimization with mutation for global numerical optimization , 2011, Appl. Math. Comput..

[14]  Gwendal Simon,et al.  Hard multidimensional multiple choice knapsack problems, an empirical study , 2010, Comput. Oper. Res..

[15]  Amir Hossein Gandomi,et al.  Opposition-based krill herd algorithm with Cauchy mutation and position clamping , 2016, Neurocomputing.

[16]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[17]  Xizhao Wang,et al.  Discrete differential evolutions for the discounted {0-1} knapsack problem , 2017, Int. J. Bio Inspired Comput..

[18]  Yu-Lin He,et al.  Exact and approximate algorithms for discounted {0-1} knapsack problem , 2016, Inf. Sci..

[19]  P. N. Suganthan,et al.  Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[20]  Zhi-Gang Ren,et al.  Fusing ant colony optimization with Lagrangian relaxation for the multiple-choice multidimensional knapsack problem , 2012, Inf. Sci..

[21]  Shengyao Wang,et al.  A novel binary fruit fly optimization algorithm for solving the multidimensional knapsack problem , 2013, Knowl. Based Syst..

[22]  Minghao Yin,et al.  Animal migration optimization: an optimization algorithm inspired by animal migration behavior , 2014, Neural Computing and Applications.

[23]  Gaige Wang,et al.  Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems , 2016, Memetic Computing.

[24]  Carmelo J. A. Bastos Filho,et al.  A novel binary artificial bee colony algorithm , 2019, Future Gener. Comput. Syst..

[25]  Kathrin Klamroth,et al.  Dynamic programming based algorithms for the discounted {0-1} knapsack problem , 2012, Appl. Math. Comput..

[26]  Leandro dos Santos Coelho,et al.  Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems , 2018, Int. J. Bio Inspired Comput..

[27]  Wenbin Li,et al.  Multi-strategy monarch butterfly optimization algorithm for discounted {0-1} knapsack problem , 2017, Neural Computing and Applications.

[28]  Yu Xue,et al.  A self-adaptive artificial bee colony algorithm based on global best for global optimization , 2017, Soft Computing.

[29]  R Bellman,et al.  DYNAMIC PROGRAMMING AND LAGRANGE MULTIPLIERS. , 1956, Proceedings of the National Academy of Sciences of the United States of America.