Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

In this paper, a robust swarm-inspired algorithm has been proposed known as Cosine algorithm (CA) to solve the optimisation problem. The CA generates several initial random agents’ solution and requires all of them to change towards or outwards the ideal solution by means of mathematical model on Cosine function. A number of adaptive and random variables are also added into this method to promote exploitation and exploration of the search space at certain optimization milestones. The results of performance metrics and test functions demonstrate that the developed algorithm is capable of successfully exploring diverse areas of a search space, avoiding local optima, converging towards the worldwide optimum and exploiting potential parts of a search space through optimisation.

Department of Mathematics

School of Advanced Sciences

Vellore Campus

Lecture Notes on Data Engineering and Communications Technologies

<a href="https://scholar.google.co.in/citations?view_op=view_citation&amp;hl=en&amp;user=cQlAeK0AAAAJ&amp;sortby=pubdate&amp;citation_for_view=cQlAeK0AAAAJ:ULOm3_A8WrAC">A Novel Cosine Swarm Algorithm for Solving Optimization Problems</a>

Whale optimization algorithm (WOA) is a novel and competitive swarm-based optimisation method that exceeds several previous metaheuristic algorithms in terms of simplicity and efficiency. Whale optimisation algorithm, a revolutionary nature-inspired algorithm, which mimics the behaviour patterns of humpback whales. WOA will interference with local optimization and greatly reduce accuracy for global optimization issue. To solve this type of problem, in this work, a new update equation has been developed named as modified whale optimisation algorithm (MWOA). Also, MWOA has been tested some CEC 2005 benchmark functions with dimension ranging from 2 to 30. The experimental outcomes show that the MWOA produce improved outcomes in terms of optimum value, convergence speed, and stability.

<a href="https://scholar.google.co.in/citations?view_op=view_citation&amp;hl=en&amp;user=cQlAeK0AAAAJ&amp;sortby=pubdate&amp;citation_for_view=cQlAeK0AAAAJ:Zph67rFs4hoC">A Modified Whale Optimisation Algorithm to Solve Global Optimisation Problems</a>

In last few decades, the emission of greenhouse gasses has exponentially increased due to large production of electric power energy from conventional fossil fuels to pose critical environmental challenges. The renewable energies (REs) are establishing themselves as key technologies for reduction of carbon emissions, in addition to low cost and high efficiency. However, the operational limits and the power generation procedures of the renewable energies invite immense challenges. The uncertainty in production with precise and error free approximation make it very complicated. Hence, an effective approach with methodical organization of the renewable energies are the need of the hour for reliable and safe system. In this study, an IEEE 30-bus hybrid power system (HPS) problem consisting of conventional thermal generators and green energies like wind generators and solar photovoltaic are considered to become environmentally and economically capable than the existing ones. Several measures like penalty cost and reserve cost have been considered in this present study for addressing the uncertainty issues underestimation and overestimation respectively. Further, three hybrid configurations such as thermal-solar (TS), thermal-wind (TW) and thermal-wind-solar (TWS) are proposed to perform the cost effective analysis. The adopted hybrid power system is extremely complex and non-linear <a href="https://www.sciencedirect.com/topics/computer-science/optimization-problem">optimization problem</a>. Hence, a recently proposed <a href="https://www.sciencedirect.com/topics/computer-science/evolutionary-algorithm">evolutionary algorithm</a> namely competitive swarm optimization (CSO) algorithm is implemented to discover the optimum result for the variety goals like minimum production cost, carbon emission, voltage variation and loss of the power. The performance of CSO algorithm is compared with several state-of-the-art meta-heuristic algorithms such GA, <a href="https://www.sciencedirect.com/topics/computer-science/particle-swarm-optimization">PSO</a>, CSA, ABC, and SHADE-SF. The extraordinary outcomes achieved in this work illustrate that the CSO method can successfully be applied to handle the complex, non-convex and non-linear hybrid power system problems.

Fulltext

Journal of King Saud University - Computer and Information Sciences Journal of King Saud University - Computer and Information Sciences

<a href="https://scholar.google.co.in/citations?view_op=view_citation&amp;hl=en&amp;user=cQlAeK0AAAAJ&amp;sortby=pubdate&amp;citation_for_view=cQlAeK0AAAAJ:3fE2CSJIrl8C">Combined Economic Emission Dispatch in Hybrid Power Systems using Competitive Swarm Optimization</a>

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 the competitive swarm optimizer (CSO) and the NSGA-II algorithm. In MOCSO, a pair wise competitive scenario is presented to achieve the dominance relationship between two particles in the population. In each pair wise competition, the particle that dominates the other particle is considered the winner and the other is consigned as the loser. The loser particles learn from the respective winner particles in each individual competition. The inspired CSO algorithm does not use any memory to remember the global best or personal best particles, hence, MOCSO does not need any external archive to store elite particles. The experimental results and statistical tests confirm the superiority of MOCSO over several state-of-the-art multi-objective algorithms in solving benchmark problems. © 2020, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

World University root node

International Journal of Applied Metaheuristic Computing

A novel multi-objective competitive swarm optimization algorithm

In this paper, a novel competitive swarm optimizer (NCSO) is presented for large-scale global optimization (LSGO) problems. The algorithm is basically motivated by the particle swarm optimizer (PSO) and competitive swarm optimizer (CSO) algorithms. Unlike PSO, CSO neither recalls the personal best position nor global best position to update the elements. In CSO, a pairwise competition tool was presented, where the element that fails the competition are updated by learning from the winner and the winner particles are just delivered to the succeeding generation. The suggested algorithm informs the winner element by an added novel scheme to increase the solution superiority. The algorithm has been accomplished on high-dimensional CEC2008 benchmark problems and sampling-based image matting problem. The experimental outcomes have revealed improved performance for the projected NCSO than the CSO and several metaheuristic algorithms. © 2020, Springer Nature Singapore Pte Ltd.

Advances in Intelligent Systems and Computing

Novel Competitive Swarm Optimizer for Sampling-Based Image Matting Problem

In this paper, an improvised competitive swarm optimizer (ICSO) is introduced for large-scale global optimization (LSGO) problems. The algorithm is fundamentally inspired by the competitive swarm optimizer (CSO) algorithm which neither remembers the personal best position nor global best position to update the particles. In CSO, a pair-wise competition mechanism was introduced, where the particle that loses the competition is updated by learning from the winner and the winner particles are simply passed to the next generation. The proposed algorithm introduces a new tri-competitive mechanism strategy to improve the solution quality. The algorithm has been performed on different dimensions of CEC2008 benchmark problems. The empirical results and analysis have shown better overall performance for the proposed ICSO than the CSO and many state-of-the-art meta-heuristic algorithms. © Springer Nature Singapore Pte Ltd. 2019

An improvised competitive swarm optimizer for large-scale optimization

In this paper, a new Inherited Competitive Swarm Optimizer (ICSO) is proposed for solving large-scale global optimization (LSGO) problems. The algorithm is basically motivated by both the human learning principles and the mechanism of competitive swarm optimizer (CSO). In human learning principle, characters pass on from parents to the offspring due to the ‘process of inheritance’. This concept of inheritance is integrated with CSO for faster convergence where the particles in the swarm undergo through a tri-competitive mechanism based on their fitness differences. The particles are thus divided into three groups namely winner, superior loser, and inferior loser group. In each instances, the particles in the loser group are guided by the winner particles in a cascade manner. The performance of ICSO has been tested over CEC2008 benchmark problems. The statistical analysis of the empirical results confirms the superiority of ICSO over many state-of-the-art algorithms including the basic CSO. © Springer Nature Singapore Pte Ltd. 2019.

Inherited competitive swarm optimizer for large-scale optimization problems

In this paper, the competitive swarm optimization (CSO) algorithm is applied for handling the economical load dispatch problem. The CSO algorithm is fundamentally encouraged by the particle swarm optimization (PSO) algorithm, but it does not memorize the personal best and global best to update the swarms. Rather in CSO algorithm, a pairwise competitive scenario was presented, where the loser particle is updated from the winner particle and the winner particles are directly accepted to the next population. The algorithm has been performed to find the generations of different units in a plant to reduce the entire fuel price and to maintain the total demand as well as the losses. The experimental study and investigations have revealed better performance for the CSO algorithm than the PSO and numerous state-of-art meta-heuristic algorithms in solving the economical power dispatch problem. © 2019 IOS Press. All rights reserved.

Asian Journal of Water, Environment and Pollution

CSO Technique for Solving the Economic Dispatch Problem Considering the Environmental Constraints

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. © 2017 Elsevier B.V.

Applied Soft Computing Journal

A modified competitive swarm optimizer for large scale optimization problems

Waste stemmed from inappropriate management is a major challenge for perishable resources. Improvement of the inappropriate management has great potential to improve the efficiency of the resources. This research aims to maximize profit and reduce resource spoilage through a fitness value approach based on the decay rate of the perishable resources. A particular type of resource whose decay rate is uniform with time is considered here and is defined as uniform perishable resource. But here in this paper it is shown that the best way to utilize those resources is to follow the first method (i.e. to pick up the best resource first). © Springer India 2016.

Mathematical model for optimization of perishable resources with uniform decay

Multi-objective optimization involves optimizing a number of objectives simultaneously, and it becomes challenging when the objectives conflict each other, i.e., the optimal solution of one objective function is different from that of other. These problems give rise to a set of trade-off optimal solutions, popularly known as Pareto-optimal solution. Due to multiplicity in solutions, these problems were proposed to be solved suitably by using evolutionary algorithms which use a population approach in search procedure. So, these types of problems are called evolutionary multi-objective optimization (EMO) for handling multi-objective optimization problems. In this paper, an average-point-based EMO algorithm has been suggested for solving multi-objective optimization problem following NSGA-II mechanism (AP-NSGA-II) that emphasizes population members that are non-dominated. Finally, it has been shown how our two primary goals, convergence to Paretooptimal solution and maintenance of diversity among solutions, have been achieved. © Springer India 2015.