Solving 0–1 Knapsack Problems by Binary Dragonfly Algorithm

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

“…For BFSP (binary fisherman search procedure), MDSFL (modified discrete shuffled frog-leaping algorithm), SBHS(simplified binary harmony search algorithm), SLC(soccer league competition algorithm) the results are reported from paper (Cobos et al, 2016). For BDA (binary dragonfly algorithm) the results are reported from (Abdel-Basset et al, 2017). For ABHS (adaptive binary harmony search (Wang et al, 2013), IBBA-RSS (injective binary bat algorithm based rough set scheme), the results are considered from their paper.…”

“…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.…”

DA-Based Parameter Optimization of Combined Kernel Support Vector Machine for Cancer Diagnosis

“…Step 8: If the dragonfly has at least one neighbor, the step vector and the position vector of the dragonfly will be calculated according to Equations (24) and (25). If not, the position vector will be updated by Equation (26).…”

“…Though some literature [20,21] indicates that combining multiple kernel functions can obtain better performance than a single kernel function, little research has provided an in-depth analysis of the performance of SVM classifier with a combined kernel function. There would therefore seem to be a definite need to systematically study the complex optimization problem in the SVM classifier with a combined kernel.In 2015, Mirjalili proposed a new meta-heuristic algorithm called the dragonfly algorithm (DA) [22], which has already been used to solve different optimization problems, such as feature selection [23,24], the knapsack problem [25], and image processing [26]. Considering that DA has an excellent global search ability and there are few studies on SVM classifier with combined kernels in the field of cancer classification, this paper proposed a novel classification algorithm based on DA and SVM classifier with a combined kernel function (DA-CKSVM) to improve the classification ability for cancer diagnosis.…”

“…In 2015, Mirjalili proposed a new meta-heuristic algorithm called the dragonfly algorithm (DA) [22], which has already been used to solve different optimization problems, such as feature selection [23,24], the knapsack problem [25], and image processing [26]. Considering that DA has an excellent global search ability and there are few studies on SVM classifier with combined kernels in the field of cancer classification, this paper proposed a novel classification algorithm based on DA and SVM classifier with a combined kernel function (DA-CKSVM) to improve the classification ability for cancer diagnosis.…”