Search

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 work proposes a metaphor-less and algorithm-specific parameter-less algorithm, named as self-adaptive population Rao algorithm, for solving the single-, multi-, and many-objective optimization problems. The proposed algorithm adapts the population size based on the improvement in the fitness value during the search process. The population is randomly divided into four sub-population groups. For each sub-population, a unique perturbation equation is randomly allocated. Each perturbation equation guides the solutions toward different regions of the search space. The performance of the proposed algorithm is examined using standard optimization benchmark problems having different characteristics in the single- and multi-objective optimization scenarios. The results of the application of the proposed algorithm are compared with those obtained by the latest advanced optimization algorithms. It is observed that the results obtained by the proposed method are superior. Furthermore, the proposed algorithm is used to identify optimum design parameters through multi-objective optimization of a fertilizer-assisted microalgae cultivation process and many-objective optimization of a compression ignition biodiesel engine system. From the results of the computational tests, it is observed that the performance of the self-adaptive population Rao algorithm is superior or competitive to the other advanced optimization algorithms. The performances of the considered bio-energy systems are improved by the application of the proposed optimization algorithm. The proposed optimization algorithm is more robust and may be easily extended to solve single-, multi-, and many-objective optimization problems of different science and engineering disciplines. [ABSTRACT FROM AUTHOR]

This literature review presents the extensive literature survey of various heat exchangers (HEs) for the design optimization using advanced optimization techniques concerning with various aspects. The chief objective of this work is to focus on the parametric design optimization of different types of HEs using advanced optimization algorithms and therefore only the research works associated with advanced optimization techniques are considered. This is the first paper which exclusively summaries the research works concerning with the parameter optimization of HEs using advanced optimization techniques. Various types of HEs considered in this review paper are shell-and-tube HEs, plate-fin HEs, fin-tube HEs and various configurations of HE networks etc. The parametric design optimization of HEs is associated with number of structural and physical parameters having highly complexity. Trial and error method is used in the general design approaches and this becomes tediously and time consuming and not having the guarantee of getting an optimum design. Therefore, for the design of HEs advanced optimization techniques are preferred. The review work on parametric design optimization was not attempted previously by taking into consideration various types of HEs therefore this review paper may turn into the complete information at one place and it may be very useful to the industrial design and successive researchers to choose the direction of their research work in the field of parameter optimization of HEs using advanced optimization algorithm. [ABSTRACT FROM AUTHOR]

This study proposes an adaptive multi-team perturbation-guiding Jaya (AMTPG-Jaya) algorithm which uses multiple teams to explore the search space. The proposed algorithm adapts the number of teams to explore the search space based on the convergence to the optimum. Furthermore, each team uses the same set of the population, and there is a different perturbation or movement equation for each team. As each team has a different perturbation scheme, the set of the moves to new positions by each team is unique. The moving equation of the worst performing team will be updated by the superiority of solutions produced by each team. The superiority of the solutions for each team is calculated based on the fitness value and boundary violations of solutions. The proposed algorithm is examined using computationally expensive constrained optimization problems taken from the CEC-2017 technical report. Computational test results have demonstrated the effectiveness of the AMTPG-Jaya algorithm when compared to the other well-known approaches. Also, a multi-objective optimization is carried out on a solar dish Stirling engine system to find the optimal thermo-economic parameters to maximize dimensionless power and thermal efficiency. The computational results revealed that the AMTPG-Jaya algorithm results are superior to those achieved by the other algorithms presented in this work. [ABSTRACT FROM AUTHOR]

A methodology based on a combinatorial mathematics-based decision-making method is proposed for evaluation of alternative flexible manufacturing systems. The methodology is developed to judge the relative merits of different flexible manufacturing systems for the industrial application considered. The proposed 'FMS selection index' evaluates and ranks flexible manufacturing systems for a given FMS selection problem. Two examples are included to illustrate the approach. [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]

In this work, the process parameters optimization problems of abrasive waterjet machining process are solved using a recently proposed metaheuristic optimization algorithm named as Jaya algorithm and its posteriori version named as multi-objective Jaya (MO-Jaya) algorithm. The results of Jaya and MO-Jaya algorithms are compared with the results obtained by other well-known optimization algorithms such as simulated annealing, particle swam optimization, firefly algorithm, cuckoo search algorithm, blackhole algorithm and bio-geography based optimization. A hypervolume performance metric is used to compare the results of MO-Jaya algorithm with the results of non-dominated sorting genetic algorithm and non-dominated sorting teaching–learning-based optimization algorithm. The results of Jaya and MO-Jaya algorithms are found to be better as compared to the other optimization algorithms. In addition, a multi-objective decision making method named PROMETHEE method is applied in this work in order to select a particular solution out-of the multiple Pareto-optimal solutions provided by MO-Jaya algorithm which best suits the requirements of the process planer. [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]

This paper proposes an elitist-Jaya algorithm for multi-objective design optimization of shell-and-tube and plate-fin heat exchangers. Jaya algorithm is a newly proposed algorithm and it is not having any algorithmic-specific parameters to be set except the common control parameters of number of iterations and population size. Elitist version of the Jaya algorithm is proposed in this paper to simultaneously optimize the total annual cost and effectiveness of the heat exchangers. Two heat exchanger design problems are considered for investigating the performance of the proposed algorithm. The same problems were earlier optimized by other researchers using genetic algorithm and modified teaching-learning-based optimization algorithm. The consequences of common controlling parameters e.g. number of iterations, population size and elite size, on the proposed algorithm’s performance is tested with various combinations of the same. The results of computational experiments proved the superiority of the proposed algorithm over the latest reported approaches used for the design optimization problems of heat exchangers. [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]

This article presents the performance of a very recently proposed Jaya algorithm on a class of constrained design optimization problems. The distinct feature of this algorithm is that it does not have any algorithm-specific control parameters and hence the burden of tuning the control parameters is minimized. The performance of the proposed Jaya algorithm is tested on 21 benchmark problems related to constrained design optimization. In addition to the 21 benchmark problems, the performance of the algorithm is investigated on four constrained mechanical design problems,i.e.robot gripper, multiple disc clutch brake, hydrostatic thrust bearing and rolling element bearing. The computational results reveal that the Jaya algorithm is superior to or competitive with other optimization algorithms for the problems considered. [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]