Adaptation of Binary Pigeon-Inspired Algorithm for Solving Multidimensional Knapsack Problem

Bolaji, Asaju La’aro; Babatunde, Balogun Sulaiman; Shola, Peter Bamidele

doi:10.1007/978-981-10-5687-1_66

Cited by 12 publications

(5 citation statements)

References 27 publications

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“…This research is the same as what is proposed by Bolaji et al (2017). In this paper, we propose another way to represent the MKP solution and to handling the MKP's constraints.…”

Section: Introductionmentioning

confidence: 91%

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Application of Pigeon Inspired Optimization for Multidimensional Knapsack Problem

Setiawan

Sadiyoko

Setiardjo

2020

IJIEEM

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The multidimensional knapsack problem (MKP) is a generalization of the classical knapsack problem, a problem for allocating a resource by selecting a subset of objects that seek for the highest profit while satisfying the capacity of knapsack constraint. The MKP have many practical applications in different areas and classified as a NP-hard problem. An exact method like branch and bound and dynamic programming can solve the problem, but its time computation increases exponentially with the size of the problem. Whereas some approximation method has been developed to produce a near-optimal solution within reasonable computational times. In this paper a pigeon inspired optimization (PIO) is proposed for solving MKP. PIO is one of the metaheuristic algorithms that is classified in population-based swarm intelligent that is developed based on the behavior of the pigeon to find its home although it had gone far away from it home. In this paper, PIO implementation to solve MKP is applied to two different characteristic cases in total 10 cases. The result of the implementation of the two-best combination of parameter values for 10 cases compared to particle swarm optimization, intelligent water drop algorithm and the genetic algorithm gives satisfactory results.

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“…This research is the same as what is proposed by Bolaji et al (2017). In this paper, we propose another way to represent the MKP solution and to handling the MKP's constraints.…”

Section: Introductionmentioning

confidence: 91%

“…Studies about the application of PIO for solving MKP have been done by Bolaji et al, 2017and Bolaji et al, 2020. Bolaji et al (2017 proposed a binary PIO for solving MKP.…”

Section: Introductionmentioning

confidence: 99%

Application of Pigeon Inspired Optimization for Multidimensional Knapsack Problem

Setiawan

Sadiyoko

Setiardjo

2020

IJIEEM

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“…Thus, we aim to find a function with the following ideal characteristics. The initial value of R is small with gradual changes; as the independent variable increases, it rapidly reaches the preset values [40].…”

Section: Adaptive Map and Compass Factor Strategymentioning

confidence: 99%

Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning

Xia

Zhang

2018

Algorithms

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Path planning of unmanned aerial vehicles (UAVs) in threatening and adversarial areas is a constrained nonlinear optimal problem which takes a great amount of static and dynamic constraints into account. Quantum-behaved pigeon-inspired optimization (QPIO) has been widely applied to such nonlinear problems. However, conventional QPIO is suffering low global convergence speed and local optimum. In order to solve the above problems, an improved QPIO algorithm, adaptive operator QPIO, is proposed in this paper. Firstly, a new initialization process based on logistic mapping method is introduced to generate the initial population of the pigeon-swarm. After that, to improve the performance of the map and compass operation, the factor parameter will be adaptively updated in each iteration, which can balance the ability between global and local search. In the final landmark operation, the gradual decreasing pigeon population-updating strategy is introduced to prevent premature convergence and local optimum. Finally, the demonstration of the proposed algorithm on UAV path planning problem is presented, and the comparison result indicates that the performance of our algorithm is better than that of particle swarm optimization (PSO), pigeon-inspired optimization (PIO), and its variants, in terms of convergence and accuracy.

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“…In DKPIO, the positions of pigeons are discretized by a two-step process: rounding and completion. Bolaji et al [63] proposed binary PIO (BPIO) in allusion to the multidimensional knapsack problem, a combinatorial optimization task. In BPIO, a binary representation model is built to match the features of the search space in a multidimensional knapsack problem.…”

Section: Variants Of Pigeon-inspired Optimizationmentioning

confidence: 99%

Advancements in pigeon-inspired optimization and its variants

Duan

Qiu

2019

Sci. China Inf. Sci.

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The returning of homing pigeons to their lofts from remote and unfamiliar locations with great accuracy remains a mystery. Pigeon-inspired optimization (PIO), which is a novel mono-objective continuous optimization algorithm, is inspired by the hidden mechanism behind the remarkable navigation capacity of homing pigeons. Since their development, PIO and its variants have been widely applied to various fields ranging from combinatorial optimization to multi-objective optimization in many areas, such as aerospace, medicine, and energy. This study aims to review the modifications of PIO from four aspects of improvement measures, namely, component replacement, operation addition, structure adjustment, and application expansion. It also summarizes the problems of existing research and plots the course of future effort.

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Adaptation of Binary Pigeon-Inspired Algorithm for Solving Multidimensional Knapsack Problem

Cited by 12 publications

References 27 publications

Application of Pigeon Inspired Optimization for Multidimensional Knapsack Problem

Application of Pigeon Inspired Optimization for Multidimensional Knapsack Problem

Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning

Advancements in pigeon-inspired optimization and its variants

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