Author
Prabhujit Mohapatra
Bio: Prabhujit Mohapatra is an academic researcher from VIT University. The author has contributed to research in topics: Swarm behaviour & Swarm intelligence. The author has an hindex of 1, co-authored 3 publications receiving 3 citations.
Papers
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TL;DR: Overall performance of MCSO is better than the PSO and many traditional algorithms which is used for solving the problem in economic load dispatch.
Abstract: In this work, the Modified competitive swarm optimizer (MCSO) is applied for solving the economic dispatch problem. This algorithm got inspiration from particle swarm optimizer (PSO) algori...
3 citations
TL;DR: The experimental results and statistical tests confirm the superiority of MOCSO over several state-of-the-art multi-objective algorithms in solving benchmark problems.
Abstract: In this article, a new algorithm, namely the multi-objective competitive swarm optimizer (MOCSO), is introduced to handle multi-objective problems. The algorithm has been principally motivated from...
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01 Oct 2017
TL;DR: A modified CSO is being proposed in this paper where two thirds of the population swarms are being updated by a tri-competitive criterion unlike CSO, which confirms the superiority of MCSO over many other state-of-the-art meta-heuristics, including CSO.
Abstract: Display Omitted The proposed work (MCSO) is motivated by the Competitive Swarm Optimizer (CSO).2/3rd of the swarm are updated in MCSO every time by a tri-competitive criteria.Both CEC 2008 and CEC 2010 benchmark functions have been solved using MCSO.Statistical results confirms the superiority of MCSO with faster convergence rate.Clearly, MCSO maintains good balance between exploration and exploitation search. In the recent literature a popular algorithm namely Competitive Swarm Optimizer (CSO) has been proposed for solving unconstrained optimization problems that updates only half of the population in each iteration. A modified CSO (MCSO) is being proposed in this paper where two thirds of the population swarms are being updated by a tri-competitive criterion unlike CSO. A small change in CSO makes a huge difference in the solution quality. The basic idea behind the proposition is to maintain a higher rate of exploration to the search space with a faster rate of convergence. The proposed MCSO is applied to solve the standard CEC2008 and CEC2013 large scale unconstrained benchmark optimization problems. The empirical results and statistical analysis confirm the better overall performance of MCSO over many other state-of-the-art meta-heuristics, including CSO. In order to confirm the superiority further, a real life problem namely sampling-based image matting problem is solved. Considering the winners of CEC 2008 and 2013, MCSO attains the second best position in the competition.
94 citations
TL;DR: In this paper, the authors proposed a new constrained ant colony optimization (ACO) algorithm with adaptive penalty (AP) method to solve the economic load dispatch (ELD) problem, which is found to be quite efficient in terms of the quality of the solutions found for ELD problems.
5 citations
TL;DR: In this paper , the authors comprehensively review the recent advances in MAELD, which covers the problem formulation and techniques applied for the solution, and the relationship between the MAELD and other ancillary services such as unit commitment, electrical vehicle and demand side management is concisely reviewed.
2 citations
03 Jul 2020
TL;DR: A new solution to the economic load dispatch (ELD) problem using a grey wolf optimization algorithm with diverse load conditions is ascertained and the validation reveals that the proposed method is a hopeful optimization tool to find generation schedules at affordable prices.
Abstract: This paper ascertains a new solution to the economic load dispatch (ELD) problem using a grey wolf optimization (GWO) algorithm with diverse load conditions. ELD has formulated as a constrained minimization problem that satisfies certain practical and realistic constraints. In this study, both quadratic and non-convex fuel cost characteristics have considered as an objective function under static and dynamic load environments. The grey wolf optimization algorithm (GWO) has adopted as an optimization tool. It carries the exploration and exploitation process in search space through encircling, hunting, and attacking, and the algorithm has incorporated into a solution repair strategy. Therefore, a new optimum generation schedule has arrived. For demonstration, two test systems have taken with from earlier reports with full consideration, such as transmission loss and valve point loading effect. The simulated results have contrasted with the results attained by previous methods, and the validation reveals that the proposed method is a hopeful optimization tool to find generation schedules at affordable prices.
2 citations