Solution to 0–1 Knapsack Problem Using Cohort Intelligence Algorithm

Kulkarni, Anand J.; Krishnasamy, Ganesh; Abraham, Ajith

doi:10.1007/978-3-319-44254-9_5

Cited by 8 publications

(6 citation statements)

References 17 publications

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“…For ACO (Ant Colony Optimization), SA(Simulated Annealing, GA(Genetic Algorithm), NGHS, SSO(Social-Spider Optimization) algorithm results are reported from paper (Zhao & Zhou, 2018). For CI (Cohort Intelligence), results are reported from (Kulkarni et al, 2017). For CWDO (complex-valued encoding wind-driven optimization) the results are considered from (Zhou et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

“…Greedy strategy based self-adaption ant colony algorithm is introduced for the 0-1 knapsack problem (Du & Zu, 2015). In addition, many algorithms have been prospered for solving 0-1 KP such as Cognitive discrete gravitational search algorithm(CDGSA) (Razavi & Sajedi, 2015), wind driven Optimization(WDO) (Zhou et al, 2017), greedy degree and expectation efficiency (Lv et al, 2016), improved monkey algorithm (IMA) (Zhou et al, 2016a), monogamous pairs genetic algorithm (MPGA) (Lim et al, 2016), hybrid greedy and particle swarm (GPSO) (Nguyen, Wang & Truong, 2016), Quantum inspired social evolution (QSE) algorithm (Pavithr, 2016), binary particle swarm optimization based on the surrogate information with proportional acceleration coefficients (Lin et al, 2016), complex-valued encoding bat algorithm (Zhou et al, 2016b), cohort intelligence (CI) algorithm (Kulkarni et al, 2017), Migrating birds optimization (MBO) algorithm (Ulker & Tongur, 2017), binary flower pollination algorithm (BFPA) (Abdel-Basset et al, 2018a), binary bat algorithm (BBA) (Rizk-Allah et al, 2018), Social-Spider Optimization(SSO) Algorithm Nguyen et al, 2017), binary monarch butterfly optimization(BMBO) (Feng et al, 2016a), Binary Dragonfly Algorithm(BDA) (Abdel-Basset at al., 2017), Binary Fisherman Search (BFS) algorithm (Cobos et al, 2016),elite opposition-flower pollination algorithm (EOFPA) (Abdel-Basset et al, 2018b), Opposition-based learning monarch butterfly optimization with Gaussian perturbation(OLMBO) (Feng et al, 2017). In respect of the importance of knapsack problem in practical applications, developing new algorithms to solve large-scale types of knapsack problem applications undoubtedly becomes a true challenge.…”

Section: Introductionmentioning

confidence: 99%

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Binary social group optimization algorithm for solving 0-1 knapsack problem

Naik¹,

Chokkalingam²

2022

10.5267/j.dsl

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In this paper, we propose the binary version of the Social Group Optimization (BSGO) algorithm for solving the 0-1 knapsack problem. The standard Social Group Optimization (SGO) is used for continuous optimization problems. So a transformation function is used to convert the continuous values generated from SGO into binary ones. The experiments are carried out using both low-dimensional and high-dimensional knapsack problems. The results obtained by the BSGO algorithm are compared with other binary optimization algorithms. Experimental results reveal the superiority of the BSGO algorithm in achieving a high quality of solutions over different algorithms and prove that it is one of the best finding algorithms especially in high-dimensional cases.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Binary social group optimization algorithm for solving 0-1 knapsack problem

Naik¹,

Chokkalingam²

2022

10.5267/j.dsl

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“…Table 2 demonstrates the dimension, capacity, weight, and profit vectors for the 0-1 knapsack. The low-dimensional instances L1 to L10 were extracted from Burkardt (n.d.) and Martello and Toth (1990), and L11 to L15 from Kulkarni et al (2017), respectively.…”

Section: -1 Knapsack Datasetmentioning

confidence: 99%

“…2. To evaluate its performance, fifteen 0-1 knapsack dataset instances from Burkardt (n.d.), Martello and Toth (1990), and Kulkarni et al (2017) were assigned. In addition, to evaluate our study against a real dataset which contain 500 applications of ransomware and 10,153 benign applications.…”

mentioning

confidence: 99%

Android ransomware detection using binary Jaya optimization algorithm

Alazab

2023

Expert Systems

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Ransomware is a serious security concern to mobile devices, as it prevents the use of the device and its contents until a ransom is paid, resulting in considerable financial losses for both people and corporations. The existing anti‐malware measures have shown to be inadequate in combatting new malware variants that utilize advanced evasion strategies like Polymorphic, Metamorphic, Dynamic Code Loading, Time‐based evasion, and Reflection. Furthermore, these primary defences have also suffered from low detection rates, significant false positives, high processing times, and excessive processing and power consumption that is inappropriate for smartphones. This paper offers the binary JAYA (BJAYA) for ransomware detection in Android mobile devices using the BJAYA optimization‐based algorithm. The developed algorithm's effectiveness has been assessed against two datasets, the 0–1 knapsack, and real ransomware dataset. The proposed BJAYA method surpassed the other algorithms on 85% of the 0–1 knapsack datasets. The suggested BJAYA method was also tested on a ransomware dataset in two phases. In the first stage of testing, BJAYA outperformed other standard classifiers with sensitivity and Gmean values of 97% and 98.2%, respectively. In the second stage of testing, BJAYA outperformed other GA, FPA, and PSO metaheuristic algorithms in terms of specificity, sensitivity, and Gmean. These findings indicate the applicability of the suggested BJAYA algorithm for ransomware detection.

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“…Furthermore, the MCA-CC algorithm has superiority over the basic MCA algorithm in finding a more quality solution with lower number of iterations in most of the high dimensional datasets. Moreover, the error rate obtained by the proposed algorithm (MCA-CC) was compared with different algorithms (Simulated Annealing (SA), Greedy Search Algorithm (GSA), Dynamic Programming (DP), Branch and Bound (BB) [17], and Cohort Intelligence (CI) [18]) on low dimension test datasets. This comparison is illustrated in Table 6.…”

mentioning

confidence: 99%

An Improved Meerkat Clan Algorithm for Solving 0-1 Knapsack Problem

Hussein

Yousif

2022

eijs

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Meerkat Clan Algorithm (MCA) is a nature-based metaheuristic algorithm which imitates the intelligent behavior of the meerkat animal. This paper presents an improvement on the MCA based on a chaotic map and crossover strategy (MCA-CC). These two strategies increase the diversification and intensification of the proposed algorithm and boost the searching ability to find more quality solutions. The 0-1 knapsack problem was solved by the basic MCA and the improved version of this algorithm (MCA-CC). The performance of these algorithms was tested on low and high dimensional problems. The experimental results demonstrate that the proposed algorithm had overcome the basic algorithm in terms of solution quality, speed and gained optimality with low dimensional problems. Furthermore, in high dimensional problems, it has competitive results in comparison with the other algorithms.

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Solution to 0–1 Knapsack Problem Using Cohort Intelligence Algorithm

Cited by 8 publications

References 17 publications

Binary social group optimization algorithm for solving 0-1 knapsack problem

Binary social group optimization algorithm for solving 0-1 knapsack problem

Android ransomware detection using binary Jaya optimization algorithm

An Improved Meerkat Clan Algorithm for Solving 0-1 Knapsack Problem

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