A binary moth search algorithm based on self-learning for multidimensional knapsack problems

Backtracking search optimization algorithm is a recent stochastic-based global search algorithm for solving real-valued numerical optimization problems. In this paper, a binary version of backtracking algorithm is proposed to deal with 0-1 optimization problems such as feature selection and knapsack problems. Feature selection is the process of selecting a subset of relevant features for use in model construction. Irrelevant features can negatively impact model performances. On the other hand, knapsack problem is a well-known optimization problem used to assess discrete algorithms. The objective of this research is to evaluate the discrete version of backtracking algorithm on the two mentioned problems and compare obtained results with other binary optimization algorithms using four usual classifiers: logistic regression, decision tree, random forest, and support vector machine. Empirical study on biological microarray data and experiments on 0-1 knapsack problems show the effectiveness of the binary algorithm and its ability to achieve good quality solutions for both problems.

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Multidimensional Knapsack Problems

10.1007/springerreference_72490 ◽

2012 ◽

Keyword(s):

Knapsack Problems ◽

Multidimensional Knapsack

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An effective hybrid harmony search-based algorithm for solving multidimensional knapsack problems

Applied Soft Computing ◽

10.1016/j.asoc.2015.01.022 ◽

2015 ◽

Vol 29 ◽

pp. 288-297 ◽

Cited By ~ 41

Author(s):

Biao Zhang ◽

Quan-Ke Pan ◽

Xin-Li Zhang ◽

Pei-Yong Duan

Keyword(s):

Harmony Search ◽

Knapsack Problems ◽

Multidimensional Knapsack

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Modified Harmony Search Algorithm for 0-1 Knapsack Problems

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.365-366.182 ◽

2013 ◽

Vol 365-366 ◽

pp. 182-185

Author(s):

Hong Gang Xia ◽

Qing Liang Wang

Keyword(s):

Convergence Rate ◽

Objective Function ◽

Objective Function Value ◽

Search Algorithm ◽

Harmony Search ◽

Harmony Search Algorithm ◽

Knapsack Problems ◽

Convergence And Stability ◽

Update Strategy ◽

Harmony Memory

In this paper, a modified harmony search (MHS) algorithm was presented for solving 0-1 knapsack problems. MHS employs position update strategy for generating new solution vectors that enhances accuracy and convergence rate of harmony search (HS) algorithm. Besides, the harmony memory consideration rate (HMCR) is dynamically adapted to the changing of objective function value in the current harmony memory, and the key parameters PAR and BW dynamically adjusted with the number of generation. Based on the experiment of solving ten classic 0-1 knapsack problems, the MHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its two improved algorithms (IHS and NGHS).

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