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Jaya algorithm is one of the recent algorithms developed to solve optimization problems. The basic concept of this algorithm consists in moving the obtained solution, for a given problem, toward the best solution and avoiding the worst one. However, it severely suffers from premature convergence problem and therefore can be easily trapped in local optimums. This study aimed to alleviate these drawbacks and improve the performance of the original Jaya algorithm. Here, three new mutation strategies were implemented in the original Jaya to improve both its global and local search abilities. Chaotic maps were proved to be able to boost the search capabilities of meta-heuristic algorithms. Therefore, after demonstrating its chaotic behavior through the sensitivity to initial conditions, topological transitivity and the density of periodic points, we proposed a new 2D cross chaotic map. The chaotic sequences provided by the proposed chaotic map were embedded into the original Jaya algorithm to generate the initial population and control the search equations. It is worth mentioning that the modifications incorporated in the original algorithm did not affect its two essential characteristics, i.e., simplicity and nonrequirement of additional control parameters. As case studies, sixteen benchmark functions were used to evaluate the performance of the proposed chaotic Jaya algorithm (C-Jaya) regarding solution accuracy and convergence speed. Comparisons with some other meta-heuristic algorithms for low-, middle- and high-dimensional benchmark functions show that the proposed C-Jaya algorithm enhances the performance of original Jaya significantly. Moreover, it offers the fastest global convergence, the highest solution quality and it is the most robust on almost all the test functions among all the algorithms. Nonparametric statistical procedures, i.e., Friedman test, Friedman aligned ranks test and Quade test, conducted to analyze the obtained results, show the superiority of the proposed algorithm.

In mechanism design with symmetrical or asymmetrical motions, obtaining high precision of the input path given by working requirements of mechanisms can be a challenge for dimensional optimization. This study proposed a novel hybrid-combined differential evolution (DE) and Jaya algorithm for the dimensional synthesis of four-bar mechanisms with symmetrical motions, called HCDJ. The suggested algorithm uses modified initialization, a hybrid-combined mutation between the classical DE and Jaya algorithm, and the elitist selection. The modified initialization allows generating initial individuals, which are satisfied with Grashof’s condition and consequential constraints. In the hybrid-combined mutation, three differential groups of mutations are combined. DE/best/1 and DE/best/2, DE/current to best/1 and Jaya operator, and DE/rand/1, and DE/rand/2 belong to the first, second, and third groups, respectively. In the second group, DE/current to best/1 is hybrid with the Jaya operator. Additionally, the elitist selection is also applied in HCDJ to find the best solutions for the next generation. To validate the feasibility of HCDJ, the numerical examples of the symmetrical motion of four-bar mechanisms are investigated. From the results, the proposed algorithm can provide accurate optimal solutions that are better than the original DE and Jaya methods, and its solutions are even better than those of many other algorithms that are available in the literature.

Discrete optimization of structures is known as a complex optimization problem with many local optima. Since metaheuristic algorithms do not require gradient information of the objective function and constraints, they are suitable for discrete optimization problems. A recently developed version of the Jaya algorithm (JA), called set-theoretical Jaya algorithm (ST-JA), has proven its effectiveness and robustness in solving structural optimization problems with continuous search spaces. In this paper, the ST-JA is applied to the discrete optimization of truss structures under stress and displacement constraints. The main idea of ST-JA is based on the division of the population of solutions into smaller well-arranged subpopulations of the same size. It follows that different subpopulations have different best and worst solutions. In this way, the ST-JA aims to strengthen both the exploration and exploitation capabilities of the classical JA and strike a balance between them. The performance of the ST-JA is demonstrated through four well-known truss optimization problems with discrete design variables, and its results are compared with those of the classical JA as well as other metaheuristic algorithms in the literature. To the best of our knowledge, this is the first time to apply ST-JA to discrete structural optimization. Numerical results reveal that ST-JA significantly outperforms the classical JA, especially in terms of convergence speed and accuracy, and provides results superior to other state-of-the-art metaheuristics.

In this study, the permutation flow shop scheduling problem with makespan criteria is investigated, and two algorithms, Jaya and SAMP-Jaya, are proposed. Jaya is a simple and effective meta-heuristic optimization technique that has been successfully applied in various engineering problems, while SAMP-Jaya is a modified version of Jaya that uses a multi-population approach. Both algorithms are parameter-less, and the ANOVA experiment is used to identify the significant control parameters. The population size is found to be the most crucial parameter for improving solution quality. The performance of Jaya and SAMP-Jaya is compared to the exact solution and the genetic algorithm, demonstrating the superiority of the proposed algorithms. The computational results confirm that the suggested optimization techniques are effective in terms of solution quality. Graphical abstract

This paper proposes a novel performance enhanced Jaya algorithm with a two group adaption (E-Jaya). Two improvements are presented in E-Jaya. First, instead of using the best and the worst values in Jaya algorithm, E-Jaya separates all candidates into two groups: the better and the worse groups based on their fitness values, then the mean of the better group and the mean of the worse group are used. Second, in order to add non algorithm-specific parameters in E-Jaya, a novel adaptive method of dividing the two groups has been developed. Finally, twelve benchmark functions with different dimensionality, such as 40, 60, and 100, were evaluated using the proposed E-Jaya algorithm. The results show that E-Jaya significantly outperformed Jaya algorithm in terms of the solution accuracy. Additionally, E-Jaya was also compared with a differential evolution (DE), a self-adapting control parameters in differential evolution (jDE), a firefly algorithm (FA), and a standard particle swarm optimization 2011 (SPSO2011) algorithm. E-Jaya algorithm outperforms all the algorithms.

Micro-wire electrical discharge machining (Micro-WEDM) process exhibits superior precision and greater relative accuracy for the efficient machining of difficult-to-machine materials. The micro-slit cutting operation using WEDM process has been experimentally investigated for the objective of analysing the average kerf-loss and responses pertaining to the economic viability of the process viz. average cutting rate and volumetric material removal rate (MRRv). The experiments are performed using a Tungsten wire of diameter 70 pm on titanium grade 5 alloy (Ti-6Al-4V). Three different controllable process variables (input parameters) associated with the Resistance-Capacitance (RC) based power generator namely discharge energy, wire feed-rate and wire travelling speed are varied to demonstrate their impacts on typical responses such as average kerf-loss, average cutting rate and MRRv. The experimental analysis revealed a close relationship that cutting rate bears with discharge energy, wire feed-rate and efficient flushing of molten liquid as well as fine debris particles. An advanced multi-objective optimization technique popularly known as Multi Objective-Jaya (MO-Jaya) algorithm has been adopted for the simultaneous optimization of average kerf-loss, average cutting rate and volumetric material removal rate. The best set of input parameters have been selected to suggest the most optimum responses for micro wire-cutting operations.

The Jaya Algorithm is a relatively new population-based optimization, which has become a progressively valuable tool in swarm intelligence. The Jaya algorithm incorporates the survival of the fittest principle alike evolutionary algorithm by its victorious nature as well as the ideal of an inducement towards a global optimal, which represents its swarm intelligence nature. Nevertheless, it has been applied in various areas of optimization, mainly in engineering practice, which is discussed and abridged based on each problem’s domain. The Jaya optimization’s vast applicability can be explained by its ability to work without any algorithm-specific parameters. The successfully solved problems may also use some of this meta-heuristic’s variants, in which the algorithm has been modified or hybridized. This paper focuses on a comprehensive review, as well as a bibliometric study of the Jaya algorithm, to imply its versatility. Hence, this study is likely to emphasize this optimization’s abilities, inspiring new researchers to make use of this simple and efficient algorithm for problem-solving.

This paper presents application of a new effective metaheuristic optimization method namely, the Jaya algorithm to deal with different optimum power flow (OPF) problems. Unlike other population-based optimization methods, no algorithm-particular controlling parameters are required for this algorithm. In this work, three goal functions are considered for the OPF solution: generation cost minimization, real power loss reduction, and voltage stability improvement. In addition, the effect of distributed generation (DG) is incorporated into the OPF problem using a modified formulation. For best allocation of DG unit(s), a sensitivity-based procedure is introduced. Simulations are carried out on the modified IEEE 30-bus and IEEE 118-bus networks to determine the effectiveness of the Jaya algorithm. The single objective optimization cases are performed both with and without DG. For all considered cases, results demonstrate that Jaya algorithm can produce an optimum solution with rapid convergence. Statistical analysis is also carried out to check the reliability of the Jaya algorithm. The optimal solution obtained by the Jaya algorithm is compared with different stochastic algorithms, and demonstrably outperforms them in terms of solution optimality and solution feasibility, proving its effectiveness and potential. Notably, optimal placement of DGs results in even better solutions.

The integration of renewable energy resources into the smart grids improves the system resilience, provide sustainable demand-generation balance, and produces clean electricity with minimal leakage currents. However, the renewable sources are intermittent in nature. Therefore, it is necessary to develop scheduling strategy to optimise hybrid PV-wind-controllable distributed generator based Microgrids in grid-connected and stand-alone modes of operation. In this manuscript, a priority-based cost optimization function is developed to show the relative significance of one cost component over another for the optimal operation of the Microgrid. The uncertainties associated with various intermittent parameters in Microgrid have also been introduced in the proposed scheduling methodology. The objective function includes the operating cost of CDGs, the emission cost associated with CDGs, the battery cost, the cost of grid energy exchange, and the cost associated with load shedding. A penalty function is also incorporated in the cost function for violations of any constraints. Multiple scenarios are generated using Monte Carlo simulation to model uncertain parameters of Microgrid (MG). These scenarios consist of the worst as well as the best possible cases, reflecting the microgrid’s real-time operation. Furthermore, these scenarios are reduced by using a k-means clustering algorithm. The reduced procedures for uncertain parameters will be used to obtain the minimum cost of MG with the help of an optimisation algorithm. In this work, a meta-heuristic approach, grey wolf optimisation (GWO), is used to minimize the developed cost optimisation function of MG. The standard LV Microgrid CIGRE test network is used to validate the proposed methodology. Results are obtained for different cases by considering different priorities to the sub-objectives using GWO algorithm. The obtained results are compared with the results of Jaya and PSO (particle swarm optimization) algorithms to validate the efficacy of the GWO method for the proposed optimization problem.

The use of castellated beams has received much attention in recent decades. Since these beams have holes in their webs, the bending moment of the cross section increases without increasing the weight of the beam. These beams are also more practical from an architectural point of view, and installations and plumbing can be passed through the holes of these beams that are used in the roof. Therefore, optimization of castellated beams is of great importance due to the increasing use of these beams in the different types of structures like parking lots, industrial buildings and warehouses, office buildings, schools and hospitals. In this study, the optimization of castellated beams with circular and hexagonal holes with cost objective function has been done using four recently developed meta-heuristic algorithms called shuffled shepherd optimization algorithm, improved shuffled-based Jaya, plasma generation optimization and set theoretical-based Jaya algorithm, and the costs for castellated beams with circular and hexagonal holes are compared. Moreover, the results show the good performance of these four new meta-algorithms in optimizing the castellated beams problems.