Jaya algorithm has recently been proposed, which is simple and efficient meta-heuristic optimization technique and has received a great attention in the world of optimization. It has been successfully applied to some thermal, design and manufacturing associated optimization problems. This paper aims to analyze the performance of Jaya algorithm for permutation flow-shop scheduling problem which is a well-known NP-hard optimization problem. The objective is to minimize the makespan. First, to make Jaya algorithm adaptive to the problem, a random priority is allocated to each job in a permutation sequence. Second, a job priority vector is converted into job permutation vector by means of Largest Order Value (LOV) rule. An exhaustive comparative study along with statistical analysis is performed by comparing the results with public benchmarks and other competitive heuristics. The key feature of Jaya algorithm of simultaneous movement towards the best solution and going away from the worst solution enables it to avoid being trapped in the local optima. Furthermore, the uniqueness of Jaya algorithm compared with any other evolutionary based optimization technique is that it is totally independent of specific parameters. This substantially reduces the computation effort and numerical complexity. Computational results reveal that Jaya algorithm is efficient in most cases and has considerable potential for permutation flow-shop scheduling problems.

A simple yet powerful optimization algorithm is proposed in this paper for solving the constrained and unconstrained optimization problems. This algorithm is based on the concept that the solution obtained for a given problem should move towards the best solution and should avoid the worst solution. This algorithm requires only the common control parameters and does not require any algorithm-specific control parameters. The performance of the proposed algorithm is investigated by implementing it on 24 constrained benchmark functions having different characteristics given in Congress on Evolutionary Computation (CEC 2006) and the performance is compared with that of other well-known optimization algorithms. The results have proved the better effectiveness of the proposed algorithm. Furthermore, the statistical analysis of the experimental work has been carried out by conducting the Friedman’s rank test and Holm-Sidak test. The proposed algorithm is found to secure first rank for the ‘best’ and ‘mean’ solutions in the Friedman’s rank test for all the 24 constrained benchmark problems. In addition to solving the constrained benchmark problems, the algorithm is also investigated on 30 unconstrained benchmark problems taken from the literature and the performance of the algorithm is found better.

The NanoMQL technique is used to overcome the limitations of wet grinding due to economic and ecological problems. The performance measure is largely influenced by the process parameters such as table speed, depth of cut, air pressure, coolant flow rate and nanofluid concentration. In this paper, the performance of NanoMQL technique in terms of surface roughness was evaluated for hard and soft EN31 steel. The Experiments were conducted by response surface methodology (RSM) using statistical software to develop regression model of surface roughness and optimization was carried out using Jaya algorithm. The result shows that lowest value of surface roughness was obtained for NanoMQL of hard steel in comparison with soft steel under grinding environ-ments such as wet, MQL and NanoMQL. Hence to improve the performance of soft steel, the modeling and optimization of surface roughness were carried out. The significant parameters were considered for model development and validity of model determined through ANOVA (Analysis of variance). Lastly, the optimal values were determined using Jaya algorithm for minimum surface roughness and the percentage error observed to be close with the experimental test.