Manjaree Pandit

Work place: Department of Electrical Engineering, Madhav Institute of Technology & Science, Gwalior-474005, INDIA



Research Interests: Data Structures and Algorithms, Computer Architecture and Organization, Neural Networks, Evolutionary Computation, Autonomic Computing


Manjaree Pandit received M.Tech. degree in Electrical Engineering from Maulana Azad College of Technology, Bhopal, India, in 1989 and the Ph.D. degree from Jiwaji University, Gwalior, India, in 2001. She is with the Department of Electrical Engineering, M.I.T.S., Gwalior. Her areas of interest are power system optimization and soft computing/evolutionary method ANN, and fuzzy neural applications to power system.

Author Articles
Single and Multi-Area Optimal Dispatch by Modified Salp Swarm Algorithm

By Deepak Kumar Sharma Hari Mohan Dubey Manjaree Pandit

DOI:, Pub. Date: 8 Jun. 2020

This paper presents modified salp swarm algorithm (MSSA) for solution of power system scheduling problems with diverse complexity level. Salp swarm algorithm (SSA) is a recently proposed efficient nature inspired (NI) optimization method inspired by foraging behaviour of salps found in deep ocean. SSA sometimes suffers to stagnation at local minima, to overcome this problem and enhancing searching capability by both exploration and exploitation MSSA is proposed in this paper. MSSA applied and tested on two types of problems. Type one is having five benchmark functions of diverse nature, whereas type two is related with real world problem of power system scheduling of a standard IEEE 114 bus system with 54 thermal units for (i) single area system, (ii) two area system and (iii) three area system. Finally Outcome of simulation results are validated with reported results by other method available in literature.

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Improved Krill Herd Algorithm with Neighborhood Distance Concept for Optimization

By Prasun Kumar Agrawal Manjaree Pandit Hari Mohan Dubey

DOI:, Pub. Date: 8 Nov. 2016

Krill herd algorithm (KHA) is a novel nature inspired (NI) optimization technique that mimics the herding behavior of krill, which is a kind of fish found in nature. The mathematical model of KHA is based on three phenomena observed in the behavior of a herd of krills, which are, moment induced by other krill, foraging motion and random physical diffusion. These three key features of the algorithm provide a good balance between global and local search capability, which makes the algorithm very powerful. The objective is to minimize the distance of each krill from the food source and also from the point of highest density of the herd, which helps in convergence of population around the food source. Improvisation has been made by introducing neighborhood distance concept along with genetic reproduction mechanism in basic KH Algorithm. KHA with mutation and crossover is called as (KHAMC) and KHA with neighborhood distance concept is referred here as (KHAMCD). This paper compares the performance of the KHA with its two improved variants KHAMC and KHAMCD. The performance of the three algorithms is compared on eight benchmark functions and also on two real world economic load dispatch (ELD) problems of power system. Results are also compared with recently reported methods to establish robustness, validity and superiority of the KHA and its variant algorithms.

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