The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Theresa R. Pope - One of the best experts on this subject based on the ideXlab platform.
-
Genetic variation in remnant populations of the woolly Spider Monkey (Brachyteles arachnoides)
International Journal of Primatology, 1998Co-Authors: Theresa R. PopeAbstract:The muriqui or woolly Spider Monkey (Brachyteles arachnoids) is an endangered primate endemic to the Atlantic Forest of Brazil,
Robert B. Wallace - One of the best experts on this subject based on the ideXlab platform.
-
towing the party line territoriality risky boundaries and male group size in Spider Monkey fission fusion societies
American Journal of Primatology, 2008Co-Authors: Robert B. WallaceAbstract:Spider Monkey communities are classic fission–fusion primate societies. I present data suggesting that Spider Monkeys (Ateles chamek) at Lago Caiman are territorial; adult males traveled further and faster than adult females and subgroup size was significantly higher in boundary areas of the Spider Monkey territory where intercommunity disputes were observed than in non-boundary areas. I then go on to examine data from 20 Ateles communities distributed across 14 study sites and five species to investigate how a series of demographic, ecological and geographical parameters influence the number of males in a given Spider Monkey community. Analyses suggest that the number of males is not significantly related to the number of females in a community, the area of the community home range, or the total perimeter length of the community boundary. However, risky boundary perimeter length, or the length of perimeter that directly borders another Spider Monkey community, explains 88% of observed variations in the number of males in each community. I discuss the results in relation to Spider Monkey ecology and territoriality, as well as the potential of this relationship for explaining chimpanzee (Pan) behavior given their extremely similar fruit specialist, male philopatry, territoriality and fission–fusion social system. Am. J. Primatol. 70:271–281, 2008. © 2007 Wiley-Liss, Inc.
-
Towing the party line: territoriality, risky boundaries and male group size in Spider Monkey fission–fusion societies
American journal of primatology, 2008Co-Authors: Robert B. WallaceAbstract:Spider Monkey communities are classic fission–fusion primate societies. I present data suggesting that Spider Monkeys (Ateles chamek) at Lago Caiman are territorial; adult males traveled further and faster than adult females and subgroup size was significantly higher in boundary areas of the Spider Monkey territory where intercommunity disputes were observed than in non-boundary areas. I then go on to examine data from 20 Ateles communities distributed across 14 study sites and five species to investigate how a series of demographic, ecological and geographical parameters influence the number of males in a given Spider Monkey community. Analyses suggest that the number of males is not significantly related to the number of females in a community, the area of the community home range, or the total perimeter length of the community boundary. However, risky boundary perimeter length, or the length of perimeter that directly borders another Spider Monkey community, explains 88% of observed variations in the number of males in each community. I discuss the results in relation to Spider Monkey ecology and territoriality, as well as the potential of this relationship for explaining chimpanzee (Pan) behavior given their extremely similar fruit specialist, male philopatry, territoriality and fission–fusion social system. Am. J. Primatol. 70:271–281, 2008. © 2007 Wiley-Liss, Inc.
Jagdish Chand Bansal - One of the best experts on this subject based on the ideXlab platform.
-
plant leaf disease identification using exponential Spider Monkey optimization
Sustainable Computing: Informatics and Systems, 2020Co-Authors: Sandeep Kumar, Harish Sharma, Vivek Kumar Sharma, B. R. Sharma, Jagdish Chand BansalAbstract:Abstract Agriculture is one of the prime sources of economy and a large community is involved in cropping various plants based on the environmental conditions. However, a number of challenges are faced by the farmers including different diseases of plants. The detection and prevention of plant diseases are the serious concern and should be treated well on time for increasing the productivity. Therefore, an automated plant disease detection system can be more beneficial for monitoring the plants. Generally, the most diseases may be detected and classified from the symptoms appeared on the leaves. For the same, extraction of relevant features plays an important role. A number of methods exists to generate high dimensional features to be used in plant disease classification problem such as SPAM, CHEN, LIU, and many more. However, generated features also include unrelated and inessential features that lead to degradation in performance and computational efficiency of a classification problem. Therefore, the choice of notable features from the high dimensional feature set is required to increase the computational efficiency and accuracy of a classifier. This paper introduces a novel exponential Spider Monkey optimization which is employed to fix the significant features from high dimensional set of features generated by SPAM. Furthermore, the selected features are fed to support vector machine for classification of plants into diseased plants and healthy plants using some important characteristics of the leaves. The experimental outcomes illustrate that the selected features by Exponential SMO effectively increase the classification reliability of the classifier in comparison to the considered feature selection approaches.
-
SocProS (1) - Chaotic Spider Monkey Optimization Algorithm with Enhanced Learning.
Advances in Intelligent Systems and Computing, 2018Co-Authors: Nirmala Sharma, Harish Sharma, Ajay Sharma, Avinash Kaur, Jagdish Chand BansalAbstract:Spider Monkey Optimization (SMO) algorithm is a category of swarm intelligence-based algorithms, which mimics the fission–fusion social system (FFSS) comportment of Spider Monkeys. Although, SMO is proven to be a balanced algorithm, i.e., it balances the exploration and exploitation phenomena, sometimes the performance of SMO is degraded due to slow convergence in the search process. This article presents an efficient modified SMO algorithm which is capable of suppressing these inadequacies and is named as chaotic Spider Monkey optimization with enhanced learning (CSMO) algorithm. In this proposed algorithm, a chaotic factor is introduced in the Global Leader stage for providing appropriate stochastic nature and enhanced learning methods are commenced over the Local Leader stage and the Local Leader Learning stage in the form of learning method and exploring method, respectively. These changes help to enhance the exploration and exploitation proficiencies of SMO algorithm. Moreover, this proposed strategy is analysed on 12 different benchmark functions, and the results are being contrasted with original SMO and two of its recent variants, namely power law-based local search in SMO (PLSMO) and Levy flight SMO (LFSMO).
-
Spider Monkey Optimization Algorithm
Studies in Computational Intelligence, 2018Co-Authors: Harish Sharma, Garima Hazrati, Jagdish Chand BansalAbstract:Foraging behavior of social creatures has always been a matter of study for the development of optimization algorithms. Spider Monkey Optimization (SMO) is a global optimization algorithm inspired by Fission-Fusion social (FFS) structure of Spider Monkeys during their foraging behavior. SMO exquisitely depicts two fundamental concepts of swarm intelligence: self-organization and division of labor. SMO has gained popularity in recent years as a swarm intelligence based algorithm and is being applied to many engineering optimization problems. This chapter presents the Spider Monkey Optimization algorithm in detail. A numerical example of SMO procedure has also been given for a better understanding of its working.
-
Optimal placement and sizing of capacitor using Limaçon inspired Spider Monkey optimization algorithm
Memetic Computing, 2017Co-Authors: Ajay Sharma, Annapurna Bhargava, Nirmala Sharma, Harish Sharma, Jagdish Chand BansalAbstract:The power system is a complex interconnected network which can be subdivided into three components: generation, distribution, and transmission. Capacitors of specific sizes are placed in the distribution network so that losses in transmission and distribution is minimum. But the decision of size and position of capacitors in this network is a complex optimization problem. In this paper, Limaçon curve inspired local search strategy (LLS) is proposed and incorporated into Spider Monkey optimization (SMO) algorithm to deal optimal placement and the sizing problem of capacitors. The proposed strategy is named as Limaçon inspired SMO (LSMO) algorithm. In the proposed local search strategy, the Limaçon curve equation is modified by incorporating the persistence and social learning components of SMO algorithm. The performance of LSMO is tested over 25 benchmark functions. Further, it is applied to solve optimal capacitor placement and sizing problem in IEEE-14, 30 and 33 test bus systems with the proper allocation of 3 and 5-capacitors. The reported results are compared with a network without a capacitor (un-capacitor) and other existing methods.
-
improving the local search ability of Spider Monkey optimization algorithm using quadratic approximation for unconstrained optimization
Computational Intelligence, 2017Co-Authors: Kavita Gupta, Kusum Deep, Jagdish Chand BansalAbstract:Spider Monkey optimization (SMO) algorithm, which simulates the food searching behavior of a swarm of Spider Monkeys, is a new addition to the class of swarm intelligent techniques for solving unconstrained optimization problems. The purpose of this article is to study the performance of SMO after incorporating quadratic approximation (QA) operator in it. The proposed version is named as QA-based Spider Monkey optimization (QASMO). An experimental study has been carried out to check the validity and applicability of QASMO. For validation purpose, the performance of QASMO is tested over a benchmark set of 46 scalable and nonscalable problems, and results are compared with the original SMO algorithm. In order to test the applicability of the proposed algorithm in solving real-life optimization problems, one of the most challenging optimization problems, namely, Lennard–Jones (LJ) problem is considered. LJ clusters containing atoms from three to ten have been taken into consideration, and results are presented. To the best of our knowledge, this is the first attempt to apply SMO and its proposed variant on a real-life problem. The results demonstrate that incorporation of QA in SMO has positive effects on its performance in terms of reliability, efficiency, and accuracy.
Nirmala Sharma - One of the best experts on this subject based on the ideXlab platform.
-
SocProS (1) - Chaotic Spider Monkey Optimization Algorithm with Enhanced Learning.
Advances in Intelligent Systems and Computing, 2018Co-Authors: Nirmala Sharma, Harish Sharma, Ajay Sharma, Avinash Kaur, Jagdish Chand BansalAbstract:Spider Monkey Optimization (SMO) algorithm is a category of swarm intelligence-based algorithms, which mimics the fission–fusion social system (FFSS) comportment of Spider Monkeys. Although, SMO is proven to be a balanced algorithm, i.e., it balances the exploration and exploitation phenomena, sometimes the performance of SMO is degraded due to slow convergence in the search process. This article presents an efficient modified SMO algorithm which is capable of suppressing these inadequacies and is named as chaotic Spider Monkey optimization with enhanced learning (CSMO) algorithm. In this proposed algorithm, a chaotic factor is introduced in the Global Leader stage for providing appropriate stochastic nature and enhanced learning methods are commenced over the Local Leader stage and the Local Leader Learning stage in the form of learning method and exploring method, respectively. These changes help to enhance the exploration and exploitation proficiencies of SMO algorithm. Moreover, this proposed strategy is analysed on 12 different benchmark functions, and the results are being contrasted with original SMO and two of its recent variants, namely power law-based local search in SMO (PLSMO) and Levy flight SMO (LFSMO).
-
Spider Monkey Optimization Algorithm with Enhanced Learning
Communications in Computer and Information Science, 2018Co-Authors: Bhagwanti, Harish Sharma, Nirmala SharmaAbstract:Spider Monkey Optimisation (SMO) is a new addition within the arena of nature-inspired algorithms. It is a recent Swarm Intelligence (SI) based algorithm, that models the food foraging behavior of a group of Spider Monkeys that mimic the Fission-Fusion Social System (FFSS) behavior. The SMO has been proven to be competitory and it balances the capabilities; exploitation and exploration efficiently. This article presents a significant variant of SMO, namely Spider Monkey Optimization with Enhanced Learning (SMOEL). In the proposed strategy, to increase the exploitation capability of SMO, an enhanced learning mechanism is introduced in the local leader stage that is based on the fitness of the solution. Reliability and accuracy of the intended algorithm are tested over 14 benchmarks functions and the comparison showed against various state of art algorithms available in the literature. The obtained outcomes prove the superiority of the intended algorithm.
-
accelerative factor based Spider Monkey optimization
International Conference on Computing Communication and Networking Technologies, 2018Co-Authors: B Bhagwanti, Harish Sharma, Nirmala SharmaAbstract:Swarm intelligence (SI) based algorithms are very efficient and popular techniques since the last decade. Spider Monkey optimisation (SMO) algorithm is a recent addition to the arena of SI. SMO is triggered from the food searching behaviour of Spider Monkeys that follow fission-fusion social system (FFSS). Although SMO has been performing very well sometimes there are some issues like stagnation, slow convergence etc. To overcome the above-mentioned issues, an efficient variant of SMO has been introduced. The proposed variant is entitled as Accelerative Factor based Spider Monkey Optimization (AFSMO) algorithm. In the proposed AFSMO to improve global convergence and get rid of stagnation, two phases of basic SMO, global leader phase, and local leader decision phase are modified by introducing an accelerative factor. This accelerative factor is decreasing the step size in an intelligent manner. Further, the efficiency and reliability of proposed AFSMO are validated over 15 different benchmark functions and Comparative study of the results of AFSMO is being done with several algorithms. The outcome of the proposed algorithm is clearly distinguishable and outperforming.
-
ICCCNT - Accelerative Factor Based Spider Monkey Optimization
2018 9th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2018Co-Authors: B Bhagwanti, Harish Sharma, Nirmala SharmaAbstract:Swarm intelligence (SI) based algorithms are very efficient and popular techniques since the last decade. Spider Monkey optimisation (SMO) algorithm is a recent addition to the arena of SI. SMO is triggered from the food searching behaviour of Spider Monkeys that follow fission-fusion social system (FFSS). Although SMO has been performing very well sometimes there are some issues like stagnation, slow convergence etc. To overcome the above-mentioned issues, an efficient variant of SMO has been introduced. The proposed variant is entitled as Accelerative Factor based Spider Monkey Optimization (AFSMO) algorithm. In the proposed AFSMO to improve global convergence and get rid of stagnation, two phases of basic SMO, global leader phase, and local leader decision phase are modified by introducing an accelerative factor. This accelerative factor is decreasing the step size in an intelligent manner. Further, the efficiency and reliability of proposed AFSMO are validated over 15 different benchmark functions and Comparative study of the results of AFSMO is being done with several algorithms. The outcome of the proposed algorithm is clearly distinguishable and outperforming.
-
Optimal placement and sizing of capacitor using Limaçon inspired Spider Monkey optimization algorithm
Memetic Computing, 2017Co-Authors: Ajay Sharma, Annapurna Bhargava, Nirmala Sharma, Harish Sharma, Jagdish Chand BansalAbstract:The power system is a complex interconnected network which can be subdivided into three components: generation, distribution, and transmission. Capacitors of specific sizes are placed in the distribution network so that losses in transmission and distribution is minimum. But the decision of size and position of capacitors in this network is a complex optimization problem. In this paper, Limaçon curve inspired local search strategy (LLS) is proposed and incorporated into Spider Monkey optimization (SMO) algorithm to deal optimal placement and the sizing problem of capacitors. The proposed strategy is named as Limaçon inspired SMO (LSMO) algorithm. In the proposed local search strategy, the Limaçon curve equation is modified by incorporating the persistence and social learning components of SMO algorithm. The performance of LSMO is tested over 25 benchmark functions. Further, it is applied to solve optimal capacitor placement and sizing problem in IEEE-14, 30 and 33 test bus systems with the proper allocation of 3 and 5-capacitors. The reported results are compared with a network without a capacitor (un-capacitor) and other existing methods.
Harish Sharma - One of the best experts on this subject based on the ideXlab platform.
-
plant leaf disease identification using exponential Spider Monkey optimization
Sustainable Computing: Informatics and Systems, 2020Co-Authors: Sandeep Kumar, Harish Sharma, Vivek Kumar Sharma, B. R. Sharma, Jagdish Chand BansalAbstract:Abstract Agriculture is one of the prime sources of economy and a large community is involved in cropping various plants based on the environmental conditions. However, a number of challenges are faced by the farmers including different diseases of plants. The detection and prevention of plant diseases are the serious concern and should be treated well on time for increasing the productivity. Therefore, an automated plant disease detection system can be more beneficial for monitoring the plants. Generally, the most diseases may be detected and classified from the symptoms appeared on the leaves. For the same, extraction of relevant features plays an important role. A number of methods exists to generate high dimensional features to be used in plant disease classification problem such as SPAM, CHEN, LIU, and many more. However, generated features also include unrelated and inessential features that lead to degradation in performance and computational efficiency of a classification problem. Therefore, the choice of notable features from the high dimensional feature set is required to increase the computational efficiency and accuracy of a classifier. This paper introduces a novel exponential Spider Monkey optimization which is employed to fix the significant features from high dimensional set of features generated by SPAM. Furthermore, the selected features are fed to support vector machine for classification of plants into diseased plants and healthy plants using some important characteristics of the leaves. The experimental outcomes illustrate that the selected features by Exponential SMO effectively increase the classification reliability of the classifier in comparison to the considered feature selection approaches.
-
Twitter sentiment analysis using hybrid Spider Monkey optimization method
Evolutionary Intelligence, 2020Co-Authors: Sayar Singh Shekhawat, Sakshi Shringi, Harish SharmaAbstract:The use of social media, over the past few years, has escalated enormously. Social media has formed a platform for the availability of abundant data. Thousands of people express their perceptions through social media. Sentiment Analysis (SA) of such views and perceptions is very substantial to measure public notion on a peculiar/specific subject matter of concern. SA is a remarkable field of data mining concerned with identification and translation of sentiments accessible on social media. Twitter is a microblogging site in which users can post updates (tweets) to friends (followers). This paper proposes a mechanism for extracting the sentiments from the tweets posted on Twitter. Tweets can be classified as positive, neutral or negative. The metaheuristic-based clustering techniques are superior to conventional techniques due to the subjective behaviour of tweets. A hybrid strategy, named as Hybrid Spider Monkey optimization with k-means clustering, is introduced to obtain the optimal cluster-heads of the dataset. The accuracy of the proposed method is determined on two datasets, namely, sender2 and twitter. To analyse the authenticity of the proposed method, a comparative analysis is performed with a few significant Nature-Inspired Algorithms such as Spider-Monkey optimization, Particle-Swarm algorithm, Genetic-Algorithm and Differential Evolution.
-
SocProS (1) - Chaotic Spider Monkey Optimization Algorithm with Enhanced Learning.
Advances in Intelligent Systems and Computing, 2018Co-Authors: Nirmala Sharma, Harish Sharma, Ajay Sharma, Avinash Kaur, Jagdish Chand BansalAbstract:Spider Monkey Optimization (SMO) algorithm is a category of swarm intelligence-based algorithms, which mimics the fission–fusion social system (FFSS) comportment of Spider Monkeys. Although, SMO is proven to be a balanced algorithm, i.e., it balances the exploration and exploitation phenomena, sometimes the performance of SMO is degraded due to slow convergence in the search process. This article presents an efficient modified SMO algorithm which is capable of suppressing these inadequacies and is named as chaotic Spider Monkey optimization with enhanced learning (CSMO) algorithm. In this proposed algorithm, a chaotic factor is introduced in the Global Leader stage for providing appropriate stochastic nature and enhanced learning methods are commenced over the Local Leader stage and the Local Leader Learning stage in the form of learning method and exploring method, respectively. These changes help to enhance the exploration and exploitation proficiencies of SMO algorithm. Moreover, this proposed strategy is analysed on 12 different benchmark functions, and the results are being contrasted with original SMO and two of its recent variants, namely power law-based local search in SMO (PLSMO) and Levy flight SMO (LFSMO).
-
Spider Monkey Optimization Algorithm with Enhanced Learning
Communications in Computer and Information Science, 2018Co-Authors: Bhagwanti, Harish Sharma, Nirmala SharmaAbstract:Spider Monkey Optimisation (SMO) is a new addition within the arena of nature-inspired algorithms. It is a recent Swarm Intelligence (SI) based algorithm, that models the food foraging behavior of a group of Spider Monkeys that mimic the Fission-Fusion Social System (FFSS) behavior. The SMO has been proven to be competitory and it balances the capabilities; exploitation and exploration efficiently. This article presents a significant variant of SMO, namely Spider Monkey Optimization with Enhanced Learning (SMOEL). In the proposed strategy, to increase the exploitation capability of SMO, an enhanced learning mechanism is introduced in the local leader stage that is based on the fitness of the solution. Reliability and accuracy of the intended algorithm are tested over 14 benchmarks functions and the comparison showed against various state of art algorithms available in the literature. The obtained outcomes prove the superiority of the intended algorithm.
-
accelerative factor based Spider Monkey optimization
International Conference on Computing Communication and Networking Technologies, 2018Co-Authors: B Bhagwanti, Harish Sharma, Nirmala SharmaAbstract:Swarm intelligence (SI) based algorithms are very efficient and popular techniques since the last decade. Spider Monkey optimisation (SMO) algorithm is a recent addition to the arena of SI. SMO is triggered from the food searching behaviour of Spider Monkeys that follow fission-fusion social system (FFSS). Although SMO has been performing very well sometimes there are some issues like stagnation, slow convergence etc. To overcome the above-mentioned issues, an efficient variant of SMO has been introduced. The proposed variant is entitled as Accelerative Factor based Spider Monkey Optimization (AFSMO) algorithm. In the proposed AFSMO to improve global convergence and get rid of stagnation, two phases of basic SMO, global leader phase, and local leader decision phase are modified by introducing an accelerative factor. This accelerative factor is decreasing the step size in an intelligent manner. Further, the efficiency and reliability of proposed AFSMO are validated over 15 different benchmark functions and Comparative study of the results of AFSMO is being done with several algorithms. The outcome of the proposed algorithm is clearly distinguishable and outperforming.