Harish Sharma

Bio: Harish Sharma is an academic researcher from Rajasthan Technical University. The author has contributed to research in topics: Swarm intelligence & Artificial bee colony algorithm. The author has an hindex of 24, co-authored 139 publications receiving 1963 citations. Previous affiliations of Harish Sharma include South Asian University & Vardhaman Mahaveer Open University.

Spider Monkey Optimization algorithm for numerical optimization

Abstract: Swarm intelligence is one of the most promising area for the researchers in the field of numerical optimization. Researchers have developed many algorithms by simulating the swarming behavior of various creatures like ants, honey bees, fish, birds and the findings are very motivating. In this paper, a new approach for numerical optimization is proposed by modeling the foraging behavior of spider monkeys. Spider monkeys have been categorized as fission–fusion social structure based animals. The animals which follow fission–fusion social systems, split themselves from large to smaller groups and vice-versa based on the scarcity or availability of food. The proposed swarm intelligence approach is named as Spider Monkey Optimization (SMO) algorithm and can broadly be classified as an algorithm inspired by intelligent foraging behavior of fission–fusion social structure based animals.

Artificial bee colony algorithm: a survey

Abstract: In recent years, swarm intelligence has proven its importance for the solution of those problems that cannot be easily dealt with classical mathematical techniques The foraging behaviour of honey bees produces an intelligent social behaviour and falls in the category of swarm intelligence Artificial bee colony ABC algorithm is a simulation of honey bee foraging behaviour, established by Karaboga in 2005 Since its inception, a lot of research has been carried out to make ABC more efficient and to apply it on different types of problems This paper presents a review on ABC developments, applications, comparative performance and future research perspectives

A Survey on Parallel Particle Swarm Optimization Algorithms

Abstract: Most of the complex research problems can be formulated as optimization problems. Emergence of big data technologies have also commenced the generation of complex optimization problems with large size. The high computational cost of these problems has rendered the development of optimization algorithms with parallelization. Particle swarm optimization (PSO) algorithm is one of the most popular swarm intelligence-based algorithm, which is enriched with robustness, simplicity and global search capabilities. However, one of the major hindrance with PSO is its susceptibility of getting entrapped in local optima and; alike other evolutionary algorithms the performance of PSO gets deteriorated as soon as the dimension of the problem increases. Hence, several efforts are made to enhance its performance that includes the parallelization of PSO. The basic architecture of PSO inherits a natural parallelism, and receptiveness of fast processing machines has made this task pretty convenient. Therefore, parallelized PSO (PPSO) has emerged as a well-accepted algorithm by the research community. Several studies have been performed on parallelizing PSO algorithm so far. Proposed work presents a comprehensive and systematic survey of the studies on PPSO algorithms and variants along with their parallelization strategies and applications.

Using Mpi Portable Parallel Programming With The Message Passing Interface

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