Cohort Intelligence Algorithm Research Articles

A Comparative performance evaluation of a complex-order PI controller for DC–DC converters

Switched-mode DC–DC converters are required to maintain constant output under uncertainties and variations in input voltage and load. The controller’s robustness is crucial in such systems, and hence various controllers have been proposed in the past few decades for DC–DC converter control. Fractional order PID controllers have attracted the attention of researchers due to their robustness and flexibility in control of power converters. Such controllers use fractional orders of integration and differentiation. Complex order controllers are the generalized form of the fractional order PID controllers and have complex orders of integration and differentiation. These controllers are very robust in the control of nonlinear systems with time-varying parameters. But complex order controllers have been very sparingly used in power electronic control. This paper proposes a complex order PI controller with a complex order integrator for controlling DC–DC buck and boost converters. The complex PID controller has four parameters to be tuned. The complex order PI controller is designed by optimization using the metaheuristic Cohort Intelligence algorithm. The results are compared with that of a fractional-order PID controller. It was observed that the complex PI controller gave a better response than the FOPID controller and was more robust to parameter variations.

Optimization of Load Frequency Control Gain Parameters for Stochastic Microgrid Power System

Interconnected multi-area microgrids are vital for the future of sustainable and reliable power systems. Effective load frequency control (LFC) is indispensable for ensuring their stable operation. This paper introduces a PID-based LFC system tailored for a stochastic microgrid with diverse power sources, including solar, wind, diesel engine generators, and electrical batteries. The gain parameters of the proposed microgrid PID LFC controller are optimized using genetic algorithms (GA), teaching learning-based optimization (TLBO), and cohort intelligence algorithms. Integral time-multiplied absolute error (ITAE) and integral time-squared error (ITSE) serve as the cost functions for all optimization algorithms. The study evaluated the performance of these optimized microgrid PID LFC configurations under random step load disruptions. Our primary findings reveal that the cohort intelligence-optimized PID LFC controller excels in minimizing computation time (upto 76% and 94% lesser than GA and TLBO respectively) and exhibits superior robust response characteristics. Moreover, the cohort intelligence algorithm requires fewer iterations (upto 66% and 90% lesser than GA and TLBO respectively) and enhances power supply quality within the multi-power microgrid electrical framework, specifically in terms of effective load frequency control.

Design of an Optimal Fractional Complex Order PID Controller for Buck Converter

Dynamic and robust controllers are the inherent requirement of power electronic converters, which are subjected to dynamic variations and nonlinearities. The effectiveness of fractional order controllers in non-linear system control has been well-established by studies in the past few decades. Various forms of fractional order controllers have been used in power-electronic control. Recent research indicates that complex order controllers, extensions of fractional controllers, are more robust against uncertainties and non-linearities than their integer and fractional order counterparts. Though complex order controllers have been employed in various nonlinear plants, they have not been extensively tested on power electronic applications. Also, the design and tuning of the controller is difficult. This paper investigates the effectiveness of a complex order PID controller on a typical power electronic DC-DC buck converter for the first time. Two types of complex order controllers of the form PI^{a+ib}D^c and PI^{a+ib}D^{c+id} were designed for a power electronic buck converter. The complex order controllers were implemented in Simulink and the optimal tuning of the complex order controller parameters for various performance indices was performed using different optimization algorithms. The Cohort Intelligence algorithm was found to give the most optimal results. Both the complex controllers showed more robustness towards uncertainties than the linear and fractional PID controllers. The PI^{a+ib}D^c controller gave the smoothest and fastest response under non-linearities. The dynamic performance of the complex order controller is the best and can be expected to be useful for more power electronic applications.

Application of cohort intelligence algorithm for goal programming problems with improved constraint handling method

Application of cohort intelligence algorithm for goal programming problems with improved constraint handling method

Multi-stopping criterion multi-feature-based multi-objective cohort intelligence algorithm for thermoacoustic engine optimisation

Multi-stopping criterion multi-feature-based multi-objective cohort intelligence algorithm for thermoacoustic engine optimisation

Multi-stopping criterion multi-feature-based multi-objective cohort intelligence algorithm for thermoacoustic engine optimisation

Multi-stopping criterion multi-feature-based multi-objective cohort intelligence algorithm for thermoacoustic engine optimisation

Optimization of advanced manufacturing processes using socio inspired cohort intelligence algorithm

The demand of Advanced Machining Processes (AMP) is continuously increasing owing to the technological advancement. The problems based on AMP are complex in nature as it consisted of parameters which are interdependent. These problems also consisted of linear and nonlinear constraints. This makes the problem complex which may not be solved using traditional optimization techniques. The optimization of process parameters is indispensable to use AMP's at its aptness and to make it economical to use. This paper states the optimization of process parameters of Ultrasonic machining (USM) and Abrasive water jet machining (AWJM) processes to maximize the Material Removal Rate (MRR) using a socio inspired Cohort Intelligent (CI) algorithm. The constraints involved with these problems are handled using static penalty function approach. The solutions are compared with other contemporary techniques such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Modified Harmony Search (HS_M) and Genetic Algorithm (GA).

Application of Cohort Intelligence Algorithm for Goal Programming Problems with Improved Constraint Handling Method

Application of Cohort Intelligence Algorithm for Goal Programming Problems with Improved Constraint Handling Method

Investigation on biomedical waste management of hospitals using cohort intelligence algorithm

• Managing Bio Medical Waste is important as it is hazardous. • Collection of waste from multiple hospitals and disposing the waste. • Optimizing routes from multiple hospitals to depot. With the innovative development of advanced technology in the field of medical, there is an enlargement in the generation of other problems such as management of biomedical waste. Hazardous waste generated from hospitals is required to be managed within time and it can be done effectively using some computer science technology. In the proposed methodology, Biomedical Waste (BMW) problem is solved with the consideration of route optimization. Route optimization is important in BMW management because while transporting the BMW from hospital to depot (disposal site) there are many types of risks associated with that route like traffic, vehicle failure, road accident etc. To avoid the dangerous effects of BMW on humans and environment, it is necessary to optimize the distance. It can help in promoting healthy and risk free life. This paper addresses the problem of finding the shortest path using Cohort Intelligence algorithm for BMW management with the consideration of human risk.

Pareto dominance based Multiobjective Cohort Intelligence algorithm

In the recent days, several novel and specialized algorithms are coming up for solving particular class of problems. However, their performance on new benchmark or real-world problem remains unsure. This paper proposes a novel Multiobjective Cohort Intelligence (MOCI) algorithm. It is based on Pareto dominance and coevolutionary design principles to achieve efficient, effective, productive and robust performance. The capability of MOCI algorithm is enhanced through use of multiple features for balance of exploration versus exploitation, search towards promising region and avoidance of search stagnation. The performance of MOCI is assessed against the state-of-the-art algorithms, such as: ARMOEA, CMOPSO, hpaEA, LMOCSO, LSMOF, NMPSO and WOFSMPSO across multiple test suites including Classical, ZDT, DTLZ, WFG and UF. The performance assessment is conducted with truly uncorrelated performance metrics. In this regard, an exploratory approach of multiple correlation analysis is proposed. Performance of MOCI algorithm is statistically verified and validated using PROMETHEE-II and nonparametric statistical tests. MOCI is capable of achieving well converged and diversified solutions on most of the test as well as real world problems. The success of MOCI is attributed to multiple features incorporated in the algorithm. In the future, MOCI could be applied to challenging problems in engineering and management.

Pareto Dominance Based Multi-objective Cohort Intelligence Algorithm

Pareto Dominance Based Multi-objective Cohort Intelligence Algorithm

Socio-Inspired Optimization of Cutting Force in Micro Drilling of CFRP Composites for Aerospace Applications

The aim of this paper is to apply socio-inspired Cohort Intelligence algorithm for the minimization of cutting force for micro drilling of CFRP composites for aerospace applications. Three objective functions developed by Ravi Shankar Anand and Karali Patra are being used. These objective functions are radial force (Fx and Fy), thrust force (Fz). Four variations of CI namely Roulette Wheel Selection, Follow Best, Follow Better, and Alienation Selection have been applied. The variations of CI was coded in MATLAB (R2016a). The results are compared with experimental work. The results obtained are much better than already available results giving significant reduction in cutting forces and thereby cutting power and improvement in hole quality. As a future direction, other metaheuristics, socio based algorithms can be applied for solving the problem. Also, variations of Cohort Intelligence can be applied for constrained problems.

Mesh smoothing of complex geometry using variations of cohort intelligence algorithm

Several approaches including optimization based methods were developed for mesh quality improvement using only node movement, keeping intact the element connectivity. In this research, a socio-inspired optimization approach referred to as cohort intelligence (CI) was investigated for mesh smoothing. Minimization of summation of condition numbers of all elements was the final aim. The geometrical boundaries of the object defined the surface and edge constraints for movement of external nodes. Movement of internal nodes was completely governed by variations of CI algorithm, viz. roulette wheel, follow best, follow better, alienation and random selection, follow worst and follow itself. The approach was demonstrated with pentagonal prism, hexagonal prism and hexagonal prism with hole. The performance of follow best and roulette wheel variations of CI algorithm was observed to be satisfactory as compared to other variations of the algorithm.

Cohort intelligence algorithm for discrete and mixed variable engineering problems

In this study, the performance of an emerging socio inspired metaheuristic optimization technique referred to as Cohort Intelligence (CI) algorithm is evaluated on discrete and mixed variable nonlinear constrained optimization problems. The investigated problems are mainly adopted from discrete structural optimization and mixed variable mechanical engineering design domains. For handling the discrete solution variables, a round off integer sampling approach is proposed. Furthermore, in order to deal with the nonlinear constraints, a penalty function method is incorporated. The obtained results are promising and computationally more efficient when compared to the other existing optimization techniques including a Multi Random Start Local Search algorithm. The associated advantages and disadvantages of CI algorithm are also discussed evaluating the effect of its two parameters namely the number of candidates, and sampling space reduction factor.

Design and economic optimization of shell-and-tube heat exchanger using cohort intelligence algorithm

Heat exchangers play a key role in wide industrial applications. Due to their complex design and high manufacturing cost, their efficient operation and optimum design are quite important for overall cost minimization. There have been several optimization algorithms developed so far for the optimum design of the shell-and-tube heat exchanger (STHE). In this paper, the ability to emerge AI-based optimization method referred to as cohort intelligence (CI) is demonstrated by solving the design and economic optimization of the STHEs. Three cases were solved. These three cases include fluids, which are different at both the shell side and tube side with different inlet and outlet temperatures at the shell side and tube side. The associated key variables such as tube outside diameter, baffle spacing, pitch size, shell inside diameter and number of tube passes that decide the total cost of the heat exchanger were optimized. The performance of the CI method is compared with existing algorithms. The quality and robustness of the CI solution at reasonable computational cost highlighted its applicability by solving real-world problems from mechanical engineering domain.

Arts and artisans at home and abroad: with sketches of the progress of foreign manufactures

With the innovative development of advanced technology in the field of medical, there is an enlargement in the generation of other problems such as management of biomedical waste. Hazardous waste generated from hospitals is required to be managed within time and it can be done effectively using some computer science technology. In the proposed methodology, Biomedical Waste (BMW) problem is solved with the consideration of route optimization. Route optimization is important in BMW management because while transporting the BMW from hospital to depot (disposal site) there are many types of risks associated with that route like traffic, vehicle failure, road accident etc. To avoid the dangerous effects of BMW on humans and environment, it is necessary to optimize the distance. It can help in promoting healthy and risk free life. This paper addresses the problem of finding the shortest path using Cohort Intelligence algorithm for BMW management with the consideration of human risk.