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This work proposes multi-objective Rao algorithms. The basic Rao algorithms are modified for solving multi-objective optimization problems. The proposed algorithms have no algorithm-specific parameters and no metaphorical meaning. Based on the interaction of the population with best, worst, and randomly selected solutions, the proposed algorithms explore the search space. The proposed algorithms handle multiple objectives simultaneously based on dominance principles and crowding distance evaluation. In addition, multi-attribute decision-making method-based selection scheme for identifying the best solutions from the Pareto fronts is included. The proposed algorithm performances are investigated on a case study of solar-assisted Brayton heat engine system and a case study of Stirling heat engine system to see whether there can be any improvement in the performances of the considered systems. Furthermore, the efficiencies of the Rao algorithms are evaluated in terms of spacing, hypervolume, and coverage metrics. The results obtained by the proposed algorithms are compared with those obtained by the latest advanced optimization algorithms. It is observed that the results obtained by the proposed algorithms are superior. The performances of the considered case studies are improved by the application of the proposed optimization algorithms. The proposed optimization algorithms are simple, robust, and can be easily implemented to solve different engineering optimization problems. [ABSTRACT FROM AUTHOR]

This study proposes an elitist-based self-adaptive multi-population (SAMPE) Jaya algorithm to solve the constrained and unconstrained problems related to numerical and engineering optimization. The Jaya algorithm is a newly developed metaheuristic-based optimization algorithm, and it does not require any algorithmic-specific parameters to be set other than the common control parameters of number of iterations and population size. The search mechanism of the Jaya algorithm is improved in this work by using the subpopulation search scheme with elitism. It uses an adaptive scheme for dividing the population into subpopulations. The effectiveness of the proposed SAMPE-Jaya algorithm is verified on CEC 2015 benchmark problems in addition to fifteen unconstrained, six constrained standard benchmark problems and four constrained mechanical design optimization problems considered from the literature. The Friedman rank test is also done for comparing the performance of the SAMPE-Jaya algorithm with other algorithms. It is also tested on three large-scale problems with the dimensions of 100, 500 and 1000. Furthermore, the proposed SAMPE-Jaya algorithm is used for solving a case study of design optimization of a micro-channel heat sink. The computational experiments have proved the effectiveness of the proposed SAMPE-Jaya algorithm. [ABSTRACT FROM AUTHOR]

This study proposes an adaptive multi-team perturbation guiding Jaya (AMTPG Jaya) algorithm. The proposed approach uses multiple teams to explore the search space. Depending on the percentage of consumed function evaluations, the AMTPG Jaya algorithm adjusts the number of teams in the search. Furthermore, all teams move from a single population set and are simultaneously guided by different perturbation equations to different regions of search space. As each team has a different perturbation scheme, the set of moves to new positions by each team is unique. During the search process, the perturbation equations are exchanged depending on the quality of the solutions produced by them. The proposed algorithm employs dominance principles and the crowding distance estimation method to handle the multiple objectives simultaneously. The proposed algorithm is examined using two multi-objective optimization case studies of a solar dish Stirling heat engine system and a multi-objective optimization case study of the Stirling heat pump. Also, the Technique for Order of Preference by Similarity to Ideal Solution decision-making method is employed for identifying an optimal solution. The computational results obtained by the proposed AMTPG Jaya algorithm are superior to those achieved by the other algorithms presented in this work. [ABSTRACT FROM AUTHOR]

Selection of optimum machining parameters is vital to the machining processes in order to ensure the quality of the product, reduce the machining cost, increasing the productivity and conserve resources for sustainability. Hence, in this work a posteriori multi-objective optimization algorithm named as Non-dominated Sorting Teaching-Learning-Based Optimization (NSTLBO) is applied to solve the multi-objective optimization problems of three machining processes namely, turning, wire-electric-discharge machining and laser cutting process and two micro-machining processes namely, focused ion beam micro-milling and micro wire-electric-discharge machining. The NSTLBO algorithm is incorporated with non-dominated sorting approach and crowding distance computation mechanism to maintain a diverse set of solutions in order to provide a Pareto-optimal set of solutions in a single simulation run. The results of the NSTLBO algorithm are compared with the results obtained using GA, NSGA-II, PSO, iterative search method and MOTLBO and are found to be competitive. The Pareto-optimal set of solutions for each optimization problem is obtained and reported. These Pareto-optimal set of solutions will help the decision maker in volatile scenarios and are useful for real production systems. [ABSTRACT FROM AUTHOR]

Welding processes are characterised as a multi-input processes. Selection of optimum combination of process parameters of a welding process is a vital task in order to achieve high quality of weld and productivity. The objective of this work is to improve the performance of submerged arc welding (SAW) process, electron beam welding (EBW) process, friction stir welding (FSW) process and gas tungsten arc welding (GTAW) process through parameter optimisation using a variant of Jaya algorithm, which is fast, robust and convenient. This variant is named as ‘Quasi-oppositional-based Jaya algorithm’ (QO-Jaya). The optimisation case studies for each of the above-mentioned welding processes are considered and the results obtained by the QO-Jaya algorithm are compared with the results obtained by well-known optimisation algorithms such as genetic algorithm (GA), simulated annealing (SA), teaching-learning-based optimisation (TLBO) and basic Jaya algorithm. [ABSTRACT FROM PUBLISHER]

Material removal rate and surface roughness are the most important performance measures in nano-finishing processes, and these are largely in uenced by the process parameters. The optimum combination of process parameters for nano-finishing processes is determined in this paper using a recently proposed optimization algorithm, named as Jaya algorithm. The results show the better performance of the Jaya algorithm over the other approaches attempted by the previous researchers such as genetic algorithm and desirability function approach for the same nano-finishing processes. The results obtained by the Jaya algorithm are useful for the real production systems. [ABSTRACT FROM AUTHOR]

This paper presents the performance of teaching–learning-based optimization (TLBO) algorithm to obtain the optimum geometrical dimensions of a robot gripper. Design of robot grippers is an important aspect of the overall robot performance and the most demanding process in any robot system to match the need for the production requirement. Robot grippers are the end effectors used to grasp and hold the objects. A gripper acts as the bridge between the robot arm and the environment around it. Five objectives, namely the difference between the maximum and minimum gripping forces, the force transmission ratio, the shift transmission ratio, length of all the elements of the gripper, and the effort of the gripper mechanism are considered as objective functions. The problem has seven design variables, including six variables as dimensions of the gripper and one variable as the angle between two links of the gripper. Three robot grippers are optimized. Computational results shows that the TLBO algorithm is better or competitive to other optimization algorithms recently reported in the literature for the considered problem. [ABSTRACT FROM PUBLISHER]

Abstract: Teaching-learning-based optimization (TLBO) is a recently developed heuristic algorithm based on the natural phenomenon of teaching-learning process. In the present work, a modified version of the TLBO algorithm is introduced and applied for the multi-objective optimization of heat exchangers. Plate-fin heat exchanger and shell and tube heat exchanger are considered for the optimization. Maximization of heat exchanger effectiveness and minimization of total cost of the exchanger are considered as the objective functions. Two examples are presented to demonstrate the effectiveness and accuracy of the proposed algorithm. The results of optimization using the modified TLBO are validated by comparing with those obtained by using the genetic algorithm (GA). [Copyright &y& Elsevier]