Solving 0–1 knapsack problems by chaotic monarch butterfly optimization algorithm with Gaussian mutation

Feng, Yanhong; Yang, Juan; Wu, Chaoling; Mei, Lu; Zhao, Xiangjun

doi:10.1007/s12293-016-0211-4

Cited by 68 publications

(22 citation statements)

References 27 publications

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“…Feng et al [14] combined chaos theory [50] with the basic MBO algorithm, and then proposed a novel chaotic MBO (CMBO) algorithm. The proposed CMBO algorithm enhanced the search e ectiveness signi cantly.…”

Section: Mbo Algorithmmentioning

confidence: 99%

“…In contrast, the monarch butter y optimization (MBO) algorithm proposed by Wang et al in 2015 [55] is a novel and promising swarm-based intelligent method [48]. Although the MBO algorithm was proposed only two years prior to the writing of this paper, some researchers have implemented several in-depth studies from algorithm improvements and engineering application [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Using Monarch Butterfly Optimization to Solve the Emergency Vehicle Routing Problem with Relief Materials in Sudden Disasters

Wang

2019

Open Geosciences

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China has one of the highest rates of natural disasters in the world. In recent years, the Chinese government has placed a high value on improving emergency natural disaster relief. The goal of this research was to resolve a key issue for emergency natural disaster relief: the emergency vehicle routing problem (EmVRP) with relief materials in sudden disasters. First, we provided a description of the EmVRP, and de ned the boundary conditions. On this basis, we constructed an optimization model of EmVRP with relief materials in sudden disasters. To reach the best solution in the least amount of time, we proposed an enhanced monarch butter y optimization (EMBO) algorithm, incorporating two modi cations to the basic MBO: a self-adaptive strategy and a crossover operator. Finally, the EMBO algorithm was used to solve the EmVRP. Our experiments using two examples EmVRP with relief materials in a sudden-onset disaster proved the suitability of EMBO. In addition, an array of comparative studies showed that the proposed EMBO algorithm can achieve satisfactory solutions in less time than the basic MBO algorithm and seven other intelligent algorithms.

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Section: Mbo Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Monarch Butterfly Optimization to Solve the Emergency Vehicle Routing Problem with Relief Materials in Sudden Disasters

Wang

2019

Open Geosciences

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“…Feng et al [98] combined chaos theory with the basic MBO algorithm, and proposed a novel chaotic MBO (CMBO) algorithm. The proposed CMBO algorithm enhanced the search effectiveness significantly.…”

Section: Related Workmentioning

confidence: 99%

Improving Monarch Butterfly Optimization Algorithm with Self-Adaptive Population

Cai

et al. 2018

Algorithms

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Abstract:Inspired by the migration behavior of monarch butterflies in nature, Wang et al. proposed a novel, promising, intelligent swarm-based algorithm, monarch butterfly optimization (MBO), for tackling global optimization problems. In the basic MBO algorithm, the butterflies in land 1 (subpopulation 1) and land 2 (subpopulation 2) are calculated according to the parameter p, which is unchanged during the entire optimization process. In our present work, a self-adaptive strategy is introduced to dynamically adjust the butterflies in land 1 and 2. Accordingly, the population size in subpopulation 1 and 2 are dynamically changed as the algorithm evolves in a linear way. After introducing the concept of a self-adaptive strategy, an improved MBO algorithm, called monarch butterfly optimization with self-adaptive population (SPMBO), is put forward. In SPMBO, only generated individuals who are better than before can be accepted as new individuals for the next generations in the migration operation. Finally, the proposed SPMBO algorithm is benchmarked by thirteen standard test functions with dimensions of 30 and 60. The experimental results indicate that the search ability of the proposed SPMBO approach significantly outperforms the basic MBO algorithm on most test functions. This also implies the self-adaptive strategy is an effective way to improve the performance of the basic MBO algorithm.

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“…The newly developed heuristics that have been used to solve real world problems include the parallel hurricane optimization algorithm [27], firefly-inspired krill herd (FKH) [28], Moth Search (MS) algorithm [29], Monarch Butterfly Optimization [30][31][32], Across Neighborhood Search (ANS) [33], Chaotic particle-swarm krill herd (CPKH) [34], Chaotic cuckoo search (CCS) [35], self-adaptive probabilistic neural network [36], and Differential Evolution algorithm (DE) [37].…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved Differential Evolution Algorithm for Crop Planning in the Northeastern Region of Thailand

Ketsripongsa

Pitakaso

Sethanan

et al. 2018

MCA

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Abstract:This research aimed to solve the economic crop planning problem, considering transportation logistics to maximize the profit from cultivated activities. Income is derived from the selling price and production rate of the plants; costs are due to operating and transportation expenses. Two solving methods are presented: (1) developing a mathematical model and solving it using Lingo v.11, and (2) using three improved Differential Evolution (DE) Algorithms-I-DE-SW, I-DE-CY, and I-DE-KV-which are DE with swap, cyclic moves (CY), and K-variables moves (KV) respectively. The algorithms were tested by 16 test instances, including this case study. The computational results showed that Lingo v.11 and all DE algorithms can find the optimal solution eight out of 16 times. Regarding the remaining test instances, Lingo v.11 was unable to find the optimal solution within 400 h. The results for the DE algorithms were compared with the best solution generated within that time. The DE solutions were 1.196-1.488% better than the best solution generated by Lingo v.11 and used 200 times less computational time. Comparing the three DE algorithms, MDE-KV was the DE that was the most flexible, with the biggest neighborhood structure, and outperformed the other DE algorithms.

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Solving 0–1 knapsack problems by chaotic monarch butterfly optimization algorithm with Gaussian mutation

Cited by 68 publications

References 27 publications

Using Monarch Butterfly Optimization to Solve the Emergency Vehicle Routing Problem with Relief Materials in Sudden Disasters

Using Monarch Butterfly Optimization to Solve the Emergency Vehicle Routing Problem with Relief Materials in Sudden Disasters

Improving Monarch Butterfly Optimization Algorithm with Self-Adaptive Population

An Improved Differential Evolution Algorithm for Crop Planning in the Northeastern Region of Thailand

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