A Novel Binary Spider Monkey Optimization Algorithm for Thinning of Concentric Circular Antenna Arrays

Singh, Urvinder; Salgotra, Rohit; Rattan, Munish

doi:10.1080/03772063.2015.1135086

Cited by 91 publications

(27 citation statements)

References 23 publications

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“…Step 5: Return the Global Leader as the final solution diabetes classifications [41], designing optimal fuzzy rule-base for a Tagaki-Sugeno-Kang (TSK) fuzzy control system [42], improving quality and diversity of particles and distributing them in particle filters to provide a robust object tracking framework [43], CDMA multiuser detection [44], optical power flow, pattern synthesis of sparse linear array and antenna arrays [45], numerical classification [46], optimizing frequency in microgrid [47], economic dispatch problem [48], optimizing models of multi-reservoir system [49], and energy efficient clustering for WSNs [50].…”

Section: Step 4: Goto Step 3 If Termination Condition Is Not Metmentioning

confidence: 99%

“…A number of studies on modified SMO for optimization problems in diverse domains have also been reported in the literature. Some of the SMO algorithms are modified to solve global optimization problems [51], to improve the local search capability of algorithm (i.e., exploitation of the search space) capability of algorithm [52], [53] and proposed fitness-based position update strategy for the spider monkeys [54], modified ageist SMO incorporating age of the monkeys that further divides the groups of monkeys into subgroups according to the age based on different levels of ability [55], binary SMO [45], modified SMO for constraint continuous optimization [37], and modified SMO incorporating Nelder-Mead method to enhance local search [51].…”

Section: Step 4: Goto Step 3 If Termination Condition Is Not Metmentioning

confidence: 99%

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Discrete Spider Monkey Optimization for Travelling Salesman Problem

Akhand

Ayon

Shahriyar

et al. 2020

Applied Soft Computing

157

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Meta-heuristic algorithms inspired by biological species have become very popular in recent years. Collective intelligence of various social insects such as ants, bees, wasps, termites, birds, fish, has been investigated to develop a number of meta-heuristic algorithms in the general domain of swarm intelligence (SI). The developed SI algorithms are found effective in solving different optimization tasks. Traveling Salesman Problem (TSP) is the combinatorial optimization problem where a salesman starting from a home city travels all the other cities and returns to home city in the shortest possible path. TSP is a popular problem due to the fact that the instances of TSP can be applied to solve real-world problems, implication of which turns TSP into a standard test bench for performance evaluation of new algorithms. Spider Monkey Optimization (SMO) is a recent addition to SI algorithms based on the social behavior of spider monkeys. SMO implicitly adopts grouping and regrouping for the interactions to improve solutions; such multi-population approach is the motivation of this study to develop an effective method for TSP. This paper presents an effective variant of SMO to solve TSP called discrete SMO (DSMO). In DSMO, every spider monkey represents a TSP solution where Swap Sequence (SS) and Swap Operator (SO) based operations are employed, which enables interaction among monkeys in obtaining the optimal TSP solution. The SOs are generated using the experience of a specific spider monkey as well as the experience of other members (local leader, global leader, or a randomly selected spider monkey) of the group. The performance and effectiveness of the proposed method have been verified on a large set of TSP instances and the outcomes are compared to other well-known methods. Experimental results demonstrate the effectiveness of the proposed DSMO for solving TSP.

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Section: Step 4: Goto Step 3 If Termination Condition Is Not Metmentioning

confidence: 99%

Section: Step 4: Goto Step 3 If Termination Condition Is Not Metmentioning

confidence: 99%

Discrete Spider Monkey Optimization for Travelling Salesman Problem

Akhand

Ayon

Shahriyar

et al. 2020

Applied Soft Computing

157

View full text Add to dashboard Cite

show abstract

“…In [29], binarization of the continuous spider monkey algorithm was performed using the logical operators AND, OR, and XOR. The proposed algorithm is designed for thinning of concentric circular antenna arrays.…”

Section: Modified Algebraic Operationsmentioning

confidence: 99%

Feature Selection Based on Swallow Swarm Optimization for Fuzzy Classification

et al. 2019

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This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric structure of a membership function. Searching for the (sub) optimal subset of features is an NP-hard problem. In this paper, a binary swallow swarm optimization (BSSO) algorithm for feature selection is proposed. To solve the classification problem, we use a fuzzy rule-based classifier. To evaluate the feature selection performance of our method, BSSO is compared to induction without feature selection and some similar algorithms on well-known benchmark datasets. Experimental results show the promising behavior of the proposed method in the optimal selection of features.

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“…The recent tendency of research goes towards algorithms inspired by nature Solve complex problems in the real world that cannot be solved with classical techniques. Now we assumed that the monkeys are younger also they are more interacting with each other and often they randomly change their position in contrast to older monkeys [3]. Now adding the latest technique of SMO included a new local research.…”

Section: Introductionmentioning

confidence: 99%

Improve the local search using Automatic Modified Position in Improved Spider Monkey Optimization Algorithm

2019

ijeat

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Spider Monkey Optimization is the new field of Swarm Intelligence. The SMO algorithms well balanced for a good exploration. Algorithm based on Spider's extraordinary behavior. Monkeys the SMO algorithm is a population-based meta-heuristic. So these articles present automatic modifying the position of the local search to improve its position. Then we say the updating algorithm called Improved Spider Monkey Optimization algorithm. Using this alternative technique we improve speed convergence. Also this algorithm tested on the problems of reference. The research paper shows proposes a productive variant of SMO that improves the Number of function. Here we have some equations to resolve these problems also we compare the result between SMO and new ISMO

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A Novel Binary Spider Monkey Optimization Algorithm for Thinning of Concentric Circular Antenna Arrays

Cited by 91 publications

References 23 publications

Discrete Spider Monkey Optimization for Travelling Salesman Problem

Discrete Spider Monkey Optimization for Travelling Salesman Problem

Feature Selection Based on Swallow Swarm Optimization for Fuzzy Classification

Improve the local search using Automatic Modified Position in Improved Spider Monkey Optimization Algorithm

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