Multi-objective Jaya Algorithm Research Articles

Optimization of measured mechanical characteristics of selective microwave hybrid heating processed Inconel 625/ SS 304 weldments using multi-objective JAYA algorithm coupled with multi-attributes decision making R-method

This work focuses on the joining of Inconel-625/SS-304 using selective microwave hybrid heating (SMHH) technique. Input power, filler powder particle size, separator, and susceptor size are considered for experimentation according to the Definitive Screening Design. The multi-objectives measured are UTS, FS, and microhardness. XRD results show the intermetallic/secondary phases, and FESEM micrographs show the metallurgical bonding occurs between base metal and filler. The joint and interface region had an average microhardness of 204 ± 10 HV and 342 ± 18 HV, respectively. The UTS and FS of the weldments measured to 550 MPa and 805 MPa. MOJAYA technique is utilized for multi-objective optimization, and R-method determined the optimal process parameters. The optimal process parameters found to 2.2 kW, 25 μm powder, 120 grit and 0.804 mm separator. The confirmation test reveals UTS − 566 MPa, FS − 903 MPa, and microhardness − 365 HV, which closely matched with predicted observations.

Dynamic pricing strategy for efficient electric vehicle charging and discharging in microgrids using multi-objective jaya algorithm

The rising demand for electric vehicle (EV) charging is spurring their increased integration into microgrids. With significant advancements, EVs have become widely adopted and integrated into various settings for charging/discharging. EVs integrated with the microgrids possess the capability to serve as variable loads and the various energy suppliers present it as a dual opportunity. However, a primary challenge in EV deployment lies in efficiently managing charging stations (CSs) to minimize waiting times for users and reduce charging costs for EV owners. In addressing these challenges require consideration of dynamic pricing mechanisms and the diverse characteristics of EVs to achieve optimal scheduling. A novel approach that combines dynamic pricing strategies with optimized scheduling for effective EV charging operations using multi-objective Jaya algorithm. To evaluate its performance, we conducted a numerical experiment using real-time data and the Nissan Leaf model EV. The results demonstrate that our multi-objective Jaya-based approach outperforms existing methods by achieving a remarkable cost saving rate of 16.013% and an average profit of ₹ 243.6331 per kilowatt-hour with a network convergence time of 112 s. Also, our proposed algorithm provides a correlation between minimized EV charging costs and maximized EV aggregator profits. These findings validate the effectiveness and practical applicability of our proposed EV scheduling algorithm in real-world scenarios.

Optimizing the fuzzy PI controller for quadruple tank system using multi-objective Jaya algorithm

This article proposes the Jaya optimization algorithm for the multi-objective optimization of parameters for a Fuzzy Proportional-Integral (Fuzzy PI) controller, referred to as MOJaya. The purpose is to control the water level of a quadruple-tank system (QTS). The controlled plant is a nonlinear and uncertain Multiple-Input Multiple-Output (MIMO) system, which is affected by various factors such as parameter uncertainties, external disturbances, and significant time delays. These factors have a considerable impact on the stability of the control system. The parameters of the fuzzy controller and the normalization coefficients of the Fuzzy PI controller are optimized offline using the multi-objective Jaya optimization (MOJaya) algorithm. To demonstrate the effectiveness of the proposed algorithm, it was compared with the Fuzzy PI and PI-MOJaya algorithms. The simulation results indicate that the proposed algorithm outperforms the other algorithms in terms of control quality.

Self-adaptive metaheuristic optimization technique for multi-objective reservoir operation

Abstract Multi-objective reservoir operation presents a number of critical challenges that must be overcome for efficient management of water resources. The inherent contradiction between several goals, such as satisfying irrigation demand and maximizing hydropower generation, is one of the major issues. Trade-offs and compromises must be carefully considered to balance these objectives. To solve this problem, a study was carried out to optimize the operation of multi-objective reservoirs with two primary goals: minimizing irrigation deficits and maximizing hydropower generation. This study employs the self-adaptive multipopulation multi-objective Jaya algorithm (SAMP-MOJA), an improved version of the Jaya algorithm, to construct an optimal Pareto Front utilizing an a priori approach. The performance of SAMP-MOJA is compared to that of other algorithms such as multi-objective particle swarm optimization, multi-objective invasive weed optimization, and multi-objective Jaya algorithm. The results of this study demonstrate that the hydropower generated by the developed model surpasses 80% of the actual generation. The study's findings will aid in designing the most effective Pareto front possible.

A decomposition-based multi-objective Jaya algorithm for lot-streaming job shop scheduling with variable sublots and intermingling setting

This study examined the lot-streaming job shop scheduling problem (LSJSP) with variable sublots and intermingling setting, which has rarely been considered in the literature and is beneficial to real industry settings. Upon variable sublots and intermingling setting, a multi-objective mixed-integer linear programming model (MILP) is formulated to achieve a tradeoff between the shortest tardiness and the minimum number of transferred sublots. Considering the NP-hard characteristic of the problem, the MILP model would get restricted in solving medium and large scale instances. Thus building an efficient multi-objective optimization algorithm is desirable. Motivated by the Jaya algorithm and decomposition idea, we propose a new algorithm framework called a decomposition based multi-objective Jaya algorithm (MOJA/D) to achieve a balance between exploration and exploitation. This MOJA/D framework can provide some guidance in algorithm construction for other multi-objective optimization problems. Following the MOJA/D framework, a specific MOJA/D metaheuristic is further designed for coping with the proposed problem. First, a novel forward and backward decoding strategy is designed to implement coevolution among different sub-problems and enhance the population diversity. Second, the Jaya updating mechanism is designed based on problem knowledge to guide the solutions moving towards the best solution and away from the worst solution. Moreover, considering the tradeoff between two conflicting objectives, the problem-specific local search strategies are designed and integrated with the neighborhood based local search strategies to enhance exploitation in the search process. The influence of parameter setting on MOJA/D is investigated by using design of experiments. The statistical results from extensive computational experiments demonstrate the effectiveness of the specific algorithm designs and show that the MOJA/D metaheuristic is superior to the existing algorithms in solving the proposed problem.

Performance of Various Voltage Stability Indices in a Stochastic Multiobjective Optimal Power Flow Using Mayfly Algorithm

The performance of voltage stability indices in the multiobjective optimal power flow of modern power systems is presented in this work. Six indices: the Voltage Collapse Proximity Index (VCPI), Line Voltage Stability Index (LVSI), Line Stability Index (Lmn), Fast Voltage Stability Index (FVSI), Line Stability Factor (LQP), and Novel Line Stability Index (NLSI) were considered as case studies on a modified IEEE 30-bus consisting of thermal, wind, solar and hybrid wind-hydro generators. A multiobjective evaluation using the multiobjective mayfly algorithm (MOMA) was performed in two operational scenarios: normal and contingency conditions, using the MATLAB–MATPOWER toolbox. Fuzzy Decision-Making technique was used to determine the best compromise solutions for each Pareto front. To evaluate the computational efficiency of the case studies, a preference selection index was used. The results indicate that VCPI and NLSI yielded the best-optimized system performance in minimizing generation costs, transmission loss reduction, and simulation time for normal and contingency conditions. The best-case studies also promoted the most scheduled reactive power generation from renewable energy sources (RES). On average, the VCPI index contributed the highest penetration level from RES (13.40%), while the Lmn index had the lowest. Overall, VCPI and Lmn index provided the best and worst average performance in both operating scenarios, respectively. Also, the MOMA algorithm demonstrated superior performance against the multiobjective harris hawks algorithm (MHHO), multiobjective Jaya algorithm (MOJAYA), multiobjective particle swarm algorithm (MOPSO), and nondominated sorting genetic algorithm III (NSGA-III) algorithms. In all, the proposed approach yields the lowest system cost and loss compared to other methods.

Multi-objective feature selection based on quasi-oppositional based Jaya algorithm for microarray data

Feature selection is the highly sought-after pre-processing technique for microarray data classification. Microarray data is a phenomenally imbalanced, high-dimensional dataset which requires a specifically designed feature selection technique. A hybrid feature selection (FS) technique is designed in this work for microarray datasets. The proposed FS technique has two phases. In the first phase, aggregate ranking of five well-known filter methods is obtained using the PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluations) method and is used to remove the unwanted features. The features selected in the first phase are then passed on to the second phase for further refinement. In the second phase, the quasi-oppositional-based multi-objective Jaya algorithm is designed for wrapper-based feature selection. Moreover, a novel quasi-opposite number generation technique for binary numbers is proposed. Ten benchmark microarray datasets are used to evaluate the performance of the proposed technique. Seven other state-of-the-art feature selection techniques are used for comparison and the proposed technique is found to outperform other techniques in terms of classification error rate and the selected number of features. In addition, the proposed FS technique is found to be ten times faster than other FS techniques.

Air quality deterministic and probabilistic forecasting system based on hesitant fuzzy sets and nonlinear robust outlier correction

Performing scientific and accurate forecasting and realizing the quantitative description of uncertainties in air quality remain challenging prospects. Because of the strong volatility and uncertainty of air pollutant concentrations, this problem increases in difficulty when multiple requirements are considered. In this study, a novel air quality deterministic and probabilistic forecasting system based on hesitant fuzzy sets and nonlinear robust outlier correction was proposed to realize air quality early warning. The proposed system solves the non-stochastic non-deterministic problem in air quality forecasting considering a novel hesitant fuzzy time series forecasting model wherein the intervals are partitioned by different approaches with optimal weights determined by the multi-objective JAYA algorithm. The forecasting performance is further enhanced with the introduction of a new nonlinear error correction model based on an outlier robust extreme learning machine and multi-objective JAYA algorithm, and the quality of the solution obtained is verified by sensitivity analysis. However, point forecast information alone is not sufficient to facilitate the rational integration of pollution control measures. Therefore, this study conducts probabilistic forecasting and constructs proper prediction intervals based on the optimal distribution of the forecasting residuals. By comparing the results with typical counterparts and comparison models considering multiple metrics, the experimental results confirmed the improvement scheme proposed in this study on the traditional fuzzy time series forecasting method while the effectiveness of applying the proposed system to air quality early warning was confirmed as well.

Multi-objective reservoir operation of the Ukai reservoir system using an improved Jaya algorithm

Abstract This paper introduces an effective and reliable approach based on a multi-population approach, namely the self-adaptive multi-population Jaya algorithm (SAMP-JA), to extract multi-purpose reservoir operation policies. The current research focused on two goals: minimizing irrigation deficits and maximizing hydropower generation. Three different models were formulated. The results were compared with those for an ordinary Jaya algorithm (JA), particle swarm optimization (PSO), and an invasive weed optimization (IWO) algorithm. In Model-1, the minimum irrigation deficit obtained by SAMP-JA and JA was 305092.99 . SAMP-JA was better than JA, PSO and IWO in terms of convergence. In Model-2, the maximum hydropower generation achieved by SAMP-JA, JA and PSO was 1723.50 . When comparing the average hydropower generation, SAMP-JA and PSO performed better than JA and IWO. In terms of convergence, SAMP-JA was better than PSO. In Model-3, a self-adaptive multi-population multi-objective Jaya algorithm (SAMP-MOJA) was better than multi-objective particle swarm optimization (MOPSO) and multi-objective Jaya algorithm (MOJA) in terms of maximum hydropower generation, and MOPSO was better than SAMP-MOJA and MOJA in terms of minimum irrigation deficiency. While comparing convergence, SAMP-MOJA was found to be better than MOPSO and MOJA. Overall, SAMP-JA was found to outperform JA, POS and IWO.

Multi-objective collaborative optimization in cement calcination process: A time domain rolling optimization method based on Jaya algorithm

Coal consumption and free calcium oxide (f-CaO) content are two important production indicators in the cement calcination process, and its collaborative optimization is of great significance to improve the production performance. However, due to the multiple dynamic interferences and multiple conflicts between coal consumption and f-CaO, product quality is unstable and coal consumption becomes high. To address the problems, this paper proposes a multi-objective collaborative optimization method for cement calcination process. This method takes the coal consumption and f-CaO content as the optimization objectives to establish a multi-objective optimization model. In addition, to solve the problem that static single-step optimization is difficult to track the continuous change of working conditions, we propose a time domain rolling multi-objective Jaya algorithm (TDRM-Jaya) to optimize the model. The algorithm, involving a population update strategy, a selection strategy and a time domain rolling strategy, realizes the dynamic rolling optimization for the cement calcination process. The test comparison with several common intelligent optimization algorithms demonstrates the advantages of using TDRM-Jaya. Besides, practical industrial data validates the effectiveness of the proposed optimization method.

Optimising the job-shop scheduling problem using a multi-objective Jaya algorithm

This paper presents an effective multi-objective Jaya (EMOJaya) algorithm to solve a multi-objective job-shop scheduling problem, aiming to simultaneously minimise the makespan, total flow time and mean tardiness. A strategy based on grey entropy parallel analysis (GEPA) is developed to assess and select solutions during the search process. To obtain a high-quality reference sequence for GEPA, an opposition-based learning (OBL) strategy is used in parallel. Additionally, the OBL strategy is incorporated into Jaya’s search operation and external archive to enhance the search ability and convergence rate of the algorithm. Computational experiments based on 30 benchmark instances with different scales confirm that GEPA and OBL can significantly improve the performance of our proposed EMOJaya. Experimental results also show that EMOJaya is able to outperform three state-of-the-art multi-objective algorithms in solving the problem at hand in terms of convergence, diversity and distribution. Further, EMOJaya can obtain more high-quality scheduling schemes, which provide more and better options for decision makers.

Multi-Objective Optimization of Switched Reluctance Machine Design Using Jaya Algorithm (MO-Jaya)

The switched reluctance machine (SRM) design is different from the design of most of other machines. SRM has many design parameters that have non-linear relationships with the performance indices (i.e., average torque, efficiency, and so forth). Hence, it is difficult to design SRM using straight forward equations with iterative methods, which is common for other machines. Optimization techniques are used to overcome this challenge by searching for the best variables values within the search area. In this paper, the optimization of SRM design is achieved using multi-objective Jaya algorithm (MO-Jaya). In the Jaya algorithm, solutions are moved closer to the best solution and away from the worst solution. Hence, a good intensification of the search process is achieved. Moreover, the randomly changed parameters achieve good search diversity. In this paper, it is suggested to also randomly change best and worst solutions. Hence, better diversity is achieved, as indicated from results. The optimization with the MO-Jaya algorithm was made for 8/6 and 6/4 SRM. Objectives used are the average torque, efficiency, and iron weight. The results of MO-Jaya are compared with the results of the non-dominated sorting genetic algorithm (NSGA-II) for the same conditions and constraints. The optimization program is made in Lua programming language and executed by FEMM4.2 software. The results show the success of the approach to achieve better objective values, a broad search, and to introduce a variety of optimal solutions.

Application of a Novel Jaya Algorithm Based on Chaotic Sequence and Opposition-based Learning in the Multi-objective Optimal Operation of Cascade Hydropower Stations System

The traditional operation of the cascade hydropower stations system (CHPS) mainly focus on the maximization of power generation benefits, but ignores the interference of CHPS operation to the river ecosystem, therefore, carrying out the multi-objective optimal operation (MOOP) of CHPS considering ecological demands is crucial. In this paper, a MOOP model considering the ecological objective is established. To effectively solve the MOOP problems, a novel multi-objective Jaya algorithm (MOCOM-Jaya) is proposed, where the quality of the initial population is enhanced based on the chaotic sequence, the later disturbance term and Gaussian mutation are incorporated to improve the local search ability, the elite opposition-based learning is adopted to broaden the optimization space. The proposed algorithm is applied to the study of MOOP of CHPS in the Wujiang river, and the results show that compared with MOPSO and NSGA-II, MOCOM-Jaya can gain the solution set with better convergence and distribution for the MOOP. The competition relationship between the power generation objective (PGO) and the ecological objective (ECO) is revealed based on the partial replacement ratio method. The results show that the competitiveness of PGO and ECO experienced a trade lead with the increase of power generation. The mean competitiveness ratios of PGO to ECO ( $\overline {CP{R_{P - E}}} $ ) in three typical years (dry, normal, wet) are 3.22, 3.17 and 3.15, indicating that the PGO is dominant in the competition with the ECO as a whole.

Identification of significant bio-markers from high-dimensional cancerous data employing a modified multi-objective meta-heuristic algorithm

Identification of the most prominent bio-markers or genes with high classification accuracy in the high-dimensional cancerous data, is still remained as an emerging challenge for the machine learning researchers. As this challenge has two objectives, i.e. minimizing the number of genes (NoG) and maximizing the classification accuracy percentage (CAP), this problem can be modeled as binary multi-objective approach. In this work, a modified version of multi-objective Jaya algorithm, multi-objective chaotic Jaya (MOCJaya), is suggested to select the minimum NoG with high CAP. Initially, a filter approach, namely Fisher score is applied to pre-select the informative genes. Then, MOCJaya algorithm is employed for both selecting key genes and classifying the cancer data. To assess the efficacy of the designed algorithm, ten binary and multi-class cancerous datasets are considered. Here, the suggested algorithm has been compared with multi-objective chaotic Genetic Algorithm (MOCGA), multi-objective chaotic particle swarm optimization (MOCPSO), multi-objective Jaya (MOJaya), multi-objective PSO (MOPSO), and non-dominated sorting GA (NSGA-II) models. Moreover, a comparison of MOCJaya algorithm with other seventeen existing models, is also performed here. The experimental results and comparison analysis reveal that MOCJaya classifies both the positive and negative samples of the cancer datasets in high CAP with a smaller NoG.

Optimized stable gait planning of biped robot using multi-objective evolutionary JAYA algorithm

This article proposes a new stable biped walking pattern generator with preset step-length value, optimized by multi-objective JAYA algorithm. The biped robot is modeled as a kinetic chain of 11 links connected by 10 joints. The inverse kinematics of the biped is applied to derive the specified biped hip and feet positions. The two objectives related to the biped walking stability and the biped to follow the preset step-length magnitude have been fully investigated and Pareto optimal front of solutions has been acquired. To demonstrate the effectiveness and superiority of proposed multi-objective JAYA, the results are compared to those of MO-PSO and MO-NSGA-2 optimization approaches. The simulation and experiment results investigated over the real small-scaled biped HUBOT-4 assert that the multi-objective JAYA technique ensures an outperforming effective and stable gait planning and walking for biped with accurate preset step-length value.

An enhanced multi-objective JAYA algorithm for U-shaped assembly line balancing considering preventive maintenance scenarios

Given that a U-shaped assembly line is usually regarded as a serial flow production system, preventive maintenance (PM) on machines in any station of the line may lead to stoppage of the whole line and sequentially cause great loss of production cost. To improve production continuity during PM, this paper proposes multiple alternative assignment plans with interchangeable abilities to ascertain the production recovery from changes of line structure. A mixed-integer mathematical model is further formulated to make decisions on alternative assignment plans. In this model, cycle time and total assignment plan alteration cost are minimised simultaneously. And an enhanced JAYA algorithm is developed to obtain well-distributed Pareto frontier solutions effectively and efficiently. The proposed algorithm suggests a similarity-based selection and various crossover/neighbourhood search operators to enhance its search ability for global optima. Numerical experiment results prove the effectiveness of the proposed model and enhanced JAYA. This novel methodology of integration optimisation bridges assembly line balancing and PM, and thus promotes production continuity and achieves economic benefits under maintenance within large-scaled assembly lines in real situations. Furthermore, a real-world case study is conducted to illustrate the significance of this novel methodology.

Multi-objective-based optimal transmission switching and demand response for managing congestion in hybrid power systems

ABSTRACT This paper proposes a novel congestion management (CM) approach by using the optimal transmission switching (OTS) and demand response (DR) for a system with conventional thermal generators and renewable energy sources (RESs). In this paper, wind and solar PV units are considered as the RESs. The stochastic behavior of wind and solar PV powers are modeled by using the appropriate probability density functions (PDFs). The proposed CM methodology simultaneously optimizes the generation dispatch, demand response, and also the network topology of the power system. The OTS identifies the branches that should be taken out of service by significantly reducing the operating cost of the system while respecting the system security. Here, the total operating cost minimization/social welfare maximization and system losses minimization are considered as the objectives to be optimized. The proposed CM problem is solved using the multi-objective Jaya algorithm and it is used to determine a set of Pareto-optimal solutions. The Jaya algorithm is simple and it does not have any algorithmic-specific parameters to be tuned. This aspect reduces the designer’s effort in tuning the parameters to arrive at the optimum objective function value. A fuzzy logic-based approach is used to identify the best compromise solution. The effectiveness of the proposed CM approach is examined on modified IEEE 30 and practical Indian 75 bus test systems. The obtained simulation results are analyzed and they show the effectiveness of the proposed approach.

Multi-Objective Jaya Algorithm for Optimal Scheduling of DGs in Distribution System Sectionalized into Multi-Microgrids

ABSTRACTIn this paper, the power dispatch problem of distributed generators (DGs) for optimal operation of the islanded distribution system has been addressed. In order to mitigate the number of customers being affected by a fault at any remote location of the system, the islanded distribution system is sectionalized into several self-supplied microgrids such that reliable power can be supplied to maximum loads. These microgrids can be operated individually or joining with other microgrids such that rescheduling of DGs can be performed for attaining desired objective. The objectives considered in this paper are minimization of operating cost of DGs, system losses, voltage deviation individually. Furthermore, a multi-objective problem is formulated considering minimization of operating cost & losses, operating cost & voltage deviation and losses & voltage deviation simultaneously. While scheduling the distribution system, dispatchable DGs are used. A novel, algorithm-specific parameter-free meta-heuristic algorithm named Jaya algorithm is used in this paper for solving the stated optimization problem. The feasibility of the proposed technique is verified with the modified 33 bus distribution network and obtained results have been validated by comparison with prior-art algorithm available in the literature.

Optimization of Abrasive Waterjet Machining Process using Multi-objective Jaya Algorithm

In this work single-objective, multi-objective and multi-parameter optimization models of a widely used modern machining process namely abrasive waterjet machining process are solved using a newly proposed optimization algorithm named Jaya algorithm. In order to solve the multi-objective optimization models, a posteriori version of Jaya algorithm named as “Multi-objective Jaya (MO-Jaya) algorithm” is used. Two optimization case studies of abrasive waterjet machining process are considered and the results of Jaya and MO-Jaya algorithms are found to be better than the results of well-known optimization algorithms such as simulated annealing (SA), particle swam optimization (PSO), firefly algorithm (FA), cuckoo search (CS) algorithm, blackhole (BH) algorithm, bio-geography based optimization (BBO) algorithm, non-dominated sorting genetic algorithm (NSGA), non-dominated sorting teaching-learning-based optimization (NSTLBO) algorithm and sequential approximation optimization (SAQ). A set of Pareto-efficient solutions is obtained for each of theconsidered multi-objective optimization problems using MO-Jaya algorithm and the same is reported in this work. Hypervolume performance metric is used to compare the quality of the Pareto-front provided by MO-Jaya algorithm to the Pareto-front provided by NSGA and NSTLBO algorithms.

A new multi-objective Jaya algorithm for optimization of modern machining processes

A new multi-objective Jaya algorithm for optimization of modern machining processes