Multi-objective Optimization Technique Research Articles

Demand side management strategy for smart building using multi-objective hybrid optimization technique

This study proposes a home energy management system that uses the load-shifting technique for demand-side management as a way to improve the energy consumption patterns of a smart house. This system's goal is to optimize the energy of household appliances in order to effectively regulate load demand, with the end result being a reduction in the peak-to-average ratio (PAR) and a consequent minimization of electricity costs. This is accomplished while also keeping user comfort as a priority. Load scheduling based on both a next-day and real-time basis is what is used to meet the load demand requested by energy customers. In addition to providing a fitness criterion, utilizing a multi-objective hybrid optimization technique makes it easier to achieve an equitable distribution of workload between on-peak and off-peak hours. Moreover, the idea of developing coordination among home appliances in order to achieve real-time rescheduling is now being studied as a concept. Because of the inherent parallels between the two problems, the real-time rescheduling issue is framed as a knapsack problem and is solved using a dynamic programming strategy. The performance of the suggested methodology is evaluated in this study in relation to real-time pricing (RTP), time-of-use pricing (ToU), and crucial peak pricing (CPP). The simulation findings, which were assessed using a confidence interval that was set at 95 %, provide proof of the relevance that has been shown to be associated with the proposed optimization method. During scheduling RTP signal showcases a minimum PAR of 2.22 and a cost reduction of 24.06 % for HAG compared to the unscheduled case. Under the TOU tariff, HAG manages to reduce PAR by 46.14 % and cost by 20.44 %. Similarly, in the case of CPP, HAG outperforms by reducing PAR by up to 29.5 % and cost by up to 31.47 %.

Harnessing the power of artificial neural networks methodology and multi-objective optimization for enhanced yield and bioactivity of plants polyphenolic compounds

The extraction of polyphenolic compounds from plants is crucial in the industrial production of functional nutraceuticals, but traditional methods often yield low and variable results. In this research, an innovative strategy for optimizing polyphenol extraction from two plants Cistus creticus L. and Ephedra alata subsp. alenda (Stapf) Trab., known for their rich composition in polyphenols and their bioactivities, using Ultrasound-Assisted Extraction in conjunction with artificial neural networks (ANNs) and multi-objective optimization is presented. ANNs were trained to model the intricate relationships among UAE parameters, including solvent concentration, temperature, and time, and the outcomes, encompassing polyphenol yield and bioactivity. Multi-objective optimization techniques were subsequently applied to identify extraction conditions that maximize both yield and bioactivity simultaneously. Results validate the accuracy of the ANNs model in predicting polyphenol yields and the significant enhancement in extraction efficiency and bioactivity achieved through multi-objective optimization. The extracts prepared in the optimal conditions have demonstrated superior antioxidant activities, compared to the non-optimized extracts, with the smallest values of IC50 of 242,378 µg/mL, and 146,736 µg/mL for the plants Ephedra alata subsp alenda (Stapf) Trab. and Cistus creticus L. respectively. This study introduces a promising approach for elevating the extraction of plant-derived polyphenols, augmenting their bioactivity with ANNs and multi-objective optimization. In light of the obtained results, it is recommended that further research explore the scalability and applicability of the presented innovative strategy in larger-scale industrial settings. Considering the demonstrated success in optimizing polyphenol extraction from Cistus creticus L. and Ephedra alata subsp. alenda (Stapf) Trab., extending the application of Ultrasound-Assisted Extraction, coupled with artificial neural networks (ANNs) and multi-objective optimization, to other plant species could offer valuable insights. Additionally, investigating the economic feasibility and environmental impact of implementing this strategy on an industrial scale would contribute to its practical viability.

ETE-SRSP: An Enhanced Optimization of Tramp Ship Routing and Scheduling

In the contemporary tramp shipping industry, route optimization and scheduling are directly linked to enhancements in operations, economics, and the environment, making them key factors for the effective management of maritime transportation. To enhance effective ship-to-cargo matching and the refinement of maritime transportation itineraries, this paper introduces a time efficiency and carbon dioxide emission multi-objective optimization algorithm named ETE-SRSP (efficiency–time–emission multi-optimization algorithm). ETE-SRSP incorporates several factors, including the initial positions of ships, time windows for loading and unloading operations, and varying sailing speeds. Within the ETE-SRSP framework, pioneering an approach that integrates ballast and laden sailing velocities as decisional parameters, it employs a multi-objective optimization technique to investigate the intricate interplay between temporal efficiency and carbon dioxide emissions. Additionally, the model’s proficiency in mitigating emissions and managing costs is clearly demonstrated through the optimization of these objectives, thereby offering a robust framework for decision support. The experimental results show that the optimal sailing speeds derived from the ETE-SRSP, under typical time-weight scenarios, can achieve an optimal balance between emission reduction and cost control. In summary, this study underscores the optimization strategy’s potential to effectively address the maritime sector’s need for economic growth and ecological conservation, showcasing its practical value in the industry.

Optimization of off-grid hybrid renewable energy systems for cost-effective and reliable power supply in Gaita Selassie Ethiopia

This paper explores scenarios for powering rural areas in Gaita Selassie with renewable energy plants, aiming to reduce system costs by optimizing component numbers to meet energy demands. Various scenarios, such as combining solar photovoltaic (PV) with pumped hydro-energy storage (PHES), utilizing wind energy with PHES, and integrating a hybrid system of PV, wind, and PHES, have been evaluated based on diverse criteria, encompassing financial aspects and reliability. To achieve the results, meta-heuristics such as the Multiobjective Gray wolf optimization algorithm (MOGWO) and Multiobjective Grasshopper optimization algorithm (MOGOA) were applied using MATLAB software. Moreover, optimal component sizing has been investigated utilizing real-time assessment data and meteorological data from Gaita Sillasie, Ethiopia. Metaheuristic optimization techniques were employed to pinpoint the most favorable loss of power supply probability (LPSP) with the least cost of energy (COE) and total life cycle cost (TLCC) for the hybrid system, all while meeting operational requirements in various scenarios. The Multi-Objective Grey Wolf Optimization (MOGWO) technique outperformed the Multi-Objective Grasshopper Optimization Algorithm (MOGOA) in optimizing the problem, as suggested by the results. Furthermore, based on MOGWO findings, the hybrid solar PV-Wind-PHES system demonstrated the lowest COE (0.126€/kWh) and TLCC (€6,897,300), along with optimal satisfaction of the village's energy demand and LPSP value. In the PV-Wind-PHSS scenario, the TLCC and COE are 38%, 18%, 2%, and 1.5% lower than those for the Wind-PHS and PV-PHSS scenarios at LPSP 0%, according to MOGWO results. Overall, this research contributes valuable insights into the design and implementation of sustainable energy solutions for remote communities, paving the way for enhanced energy access and environmental sustainability.

Elite Multi-Criteria Decision Making—Pareto Front Optimization in Multi-Objective Optimization

Optimization is a process of minimizing or maximizing a given objective function under specified constraints. In multi-objective optimization (MOO), multiple conflicting functions are optimized within defined criteria. Numerous MOO techniques have been developed utilizing various meta-heuristic methods such as Evolutionary Algorithms (EAs), Genetic Algorithms (GAs), and other biologically inspired processes. In a cooperative environment, a Pareto front is generated, and an MOO technique is applied to solve for the solution set. On other hand, Multi-Criteria Decision Making (MCDM) is often used to select a single best solution from a set of provided solution candidates. The Multi-Criteria Decision Making–Pareto Front (M-PF) optimizer combines both of these techniques to find a quality set of heuristic solutions. This paper provides an improved version of the M-PF optimizer, which is called the elite Multi-Criteria Decision Making–Pareto Front (eMPF) optimizer. The eMPF method uses an evolutionary algorithm for the meta-heuristic process and then generates a Pareto front and applies MCDM to the Pareto front to rank the solutions in the set. The main objective of the new optimizer is to exploit the Pareto front while also exploring the solution area. The performance of the developed method is tested against M-PF, Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), and Non-Dominated Sorting Genetic Algorithm-III (NSGA-III). The test results demonstrate the performance of the new eMPF optimizer over M-PF, NSGA-II, and NSGA-III. eMPF was not only able to exploit the search domain but also was able to find better heuristic solutions for most of the test functions used.

Intelligent mission planning of sky survey based on the NSGA-III algorithm

In recent years, China has been scheduled to launch an all-sky survey telescope for celestial observation. The meticulous arrangement of observation sequences for this telescope is of paramount importance, given the myriad challenges and constraints inherent to such astronomical survey missions. These challenges encompass factors such as spatial limitations, stray light contamination, as well as constraints related to data storage and energy consumption. This undertaking presents a quintessential NP-hard optimization problem. To address this complexity and enhance in-orbit observation efficiency, this study closely adheres to practical engineering considerations. A comprehensive evaluation of diverse constraints is conducted, culminating in the establishment of an optimization model for systematic planning of sky survey missions. The primary optimization objectives encompass maximizing in-orbit observation duration, augmenting the count of observable celestial points, and minimizing maneuvering angles. To tackle this multifaceted problem, a mission planning algorithm is devised based on NSGA-III, a multi-objective optimization technique. The experimental results indicate that the sky survey planning algorithm proposed in this paper can effectively accomplish sky survey observation tasks, achieving the scientific objective of no fewer than 350 daily observation points. It holds the potential to provide algorithmic support for the sky survey telescope mission on the Chinese space station.

An optimization approach for geometry design of multi-axis wave energy converter

The current global energy crisis necessitates a shift to renewable energy sources to mitigate climate change impacts. Wave energy emerges as a promising renewable resource to fulfill increasing energy demands. This energy can be extracted using wave energy converters (WECs), with multi-axis WECs (MA-WECs) being more effective than single-axis versions due to their capacity to harness energy from waves in various directions. The challenge lies in determining the ideal geometric design for MA-WECs, that can be tackled through multi-objective optimization (MOO) techniques. This research focuses on evaluating different MOO algorithms for the optimal geometric design of MA-WECs. To assess the structural response of different geometries and sizes, the study utilized the NEMOH boundary element method solver, aiming to maximize power output, lower the levelized cost of energy (LCOE), and optimize the geometry configuration. Findings indicate that the choice of optimization algorithm considerably influences the MA-WEC's optimal design, enhancing power efficiency, reducing device volume, and cutting costs more effectively than the initial design.

Sizing and energy management of grid-connected hybrid renewable energy systems based on techno-economic predictive technique

This study intends to construct and optimally run a grid-connected hybrid renewable energy system (HRES) powering a residential building. A special techno-economic multi-objective optimization technique is used for sizing the HRES. In addition, an energy management strategy (EMS) is established as a distinct multi-objective optimization problem to minimize degradation and operating costs while improving system performance. A Bi-level mixed integer non-linear programming (BMINLP) is the framework used to formulate the two-optimization problems. Sizing problem is characterized by the higher level, on contrast; the nested EMS problem within the constraints of the size problem is presented on the lower level. For this investigation, the problem is executed via the MATLAB program. The global optimization toolbox is applied via the Multi-Objective Genetic Algorithm (MOGA) to solve the sizing optimization. While the EMS is executed using the "Intlinprg" mixed integer linear programming (MILP) solver. The technical and economic performance parameters such as degradation, renewable energy fraction (REF), operating, maintenance, and investment costs are evaluated in this study. The primary results reveal that the summer week encounters the lowest total cost and the highest REF, at roughly 99484 $ and 60%, respectively. Furthermore, the investigation shows that the lowest degradation value attained in the same week with approximately 50.

Estimation single output with a hybrid of ANFIS and MOPSO_HS

In the field of soft computing, the Adaptive Neuro-Fuzzy Inference System (ANFIS) has been more well-liked in recent years for its predictive capabilities. Appropriate ANFIS parameter adjusting is critical, which creates a gap in its predictive integration with traditional optimization techniques. Although some academics have concentrated on incorporating single-objective optimization, they frequently encounter issues with reliability and stability when striving to solve problems. In this work, an innovative multi-objective optimization technique that integrates ANFIS with MOPSO_HS is introduced. The model has consistency in problem solving and shows accurate predictions for both odd and even interval input models. In addition, three actual datasets are used to demonstrate the effectiveness of the suggested model's integration. A comparison is made between the suggested integrated model and established algorithms after 20 runs of analysis. The algorithm's accuracy, stability, and dependability in resolving integration problems are demonstrated by the results, which also show how superior it is to alternative approaches.

Improving performance in swarm robots using multi-objective optimization

This paper presents a localization task for a robot swarm guided by the RAOI behavioral rules (repulsion, attraction, orientation, and influence). Slight changes in these parameters can significantly affect the task’s performance. To address this challenge, we have developed a swarm simulator incorporating the robots’ dynamic model. The localization task is evaluated using objective functions, represented as metrics, which are minimized using multi-objective optimization techniques. Our results showcase the Pareto fronts, illustrating how the objective functions react to variations in the RAOI parameters, aiming to expedite target localization while maintaining the swarm’s integrity.

Tactical Optimization of Soccer Game Based on Motion Trajectory Analysis Algorithm

Tactical optimization of soccer games using motion trajectory analysis algorithms involves the utilization of advanced computational techniques to analyze player movements and optimize team strategies. By applying motion trajectory analysis algorithms to tracking data obtained from sensors or video feeds, coaches and analysts can gain insights into player positioning, ball movement patterns, and team interactions during matches. This analysis helps identify tactical strengths and weaknesses, assess opponent strategies, and inform adjustments to game plans in real-time or during post-match analysis. By leveraging these algorithms, teams can refine their tactics, improve performance, and gain a competitive edge on the field. This paper introduces the Motion Trajectory Trigonometric Multi-Objective (MTTM-O) framework as a comprehensive approach to optimizing tactical decisions and predicting match outcomes in soccer. With trigonometric functions to model player trajectories and multi-objective optimization techniques, MTTM-O offers insights into the impact of tactical formations, expected goals, ball possession, and defensive solidity on match results. Through scenario analysis and multi-objective optimization, we demonstrate the effectiveness of MTTM-O in enhancing strategic decision-making and performance analysis in soccer. Findings underscore the importance of tactical flexibility, adaptation to opponent strategies, and the balance between offensive and defensive aspects of gameplay for success on the field. Through multi-objective optimization, MTTM-O evaluates various tactical formations and their impact on match results. For instance, in scenario analysis, employing a 3-5-2 formation resulted in an expected goal (xG) of 2.2 and a ball possession percentage of 62%, leading to a decisive win. Conversely, a 4-4-2 formation yielded lower xG (0.8) and ball possession (47%), resulting in a draw. These findings highlight the importance of tactical flexibility and adaptation to opponent strategies for success on the field.

Multi-objective optimization method for steel frame with top-and-seat angles considering yield control

Based on the yield mechanism control principle of “strong columns and weak beams, strong joints and weak components” in steel frames connected by top-and-seat steel angles, this paper proposes a method to define the relationship between the bending capacity of various components through inequality iteration and deduction. A number of component parameters has been identified that enables control of the yielding sequence in structural components. The study also developed an objective function and used genetic algorithms to create a multi-objective optimization technique for semi-rigid steel frame structures. The method considers the non-dominated cost interval, resulting in a more comprehensive approach. From the optimization case studies against the published experimental results and finite element simulations, it demonstrates that the proposed approach efficiently manages the yielding sequence and minimizes the decision space. The optimization model provides the coordination optimization between structural cost and lateral base shear capacity.

A review on simulation based multi-objective optimization of space layout design parameters on building energy performance

Improving the energy performance of buildings is crucial for environmental protection, energy savings, and a better living environment. The growing emphasis on sustainable building practices has led to an increased focus on optimizing space layout design parameters to enhance building energy performance. This review explores the application of simulation-based multi-objective optimization techniques in the context of studying the impact of space layout design on building energy efficiency. The integration of advanced simulation tools with optimization algorithms allows for a comprehensive analysis of multiple conflicting objectives like energy performance, user comfort as well as cost factor. The review begins by outlining the key parameters influencing building energy performance, including spatial configurations, orientation, and space perimeter variables. Subsequently, it delves into the various simulation tools employed to model the complex interactions between these parameters and their effects on energy performance. The integration of energy simulation software is highlighted as a crucial step towards achieving accurate and realistic assessments. In summary, this review delivers a comprehensive overview of the state-of-the-art methods in simulation-based multi-objective optimization for studying space layout design parameters and their impact on building energy performance, offering insights for researchers, practitioners, and policymakers in the field of sustainable architecture. There is a requirement for a comprehensive multi-objective framework for complex structures in the investigation of building energy performance giving more focus on reducing the cooling load and optimization of space layout along with envelope parameters.

A metaheuristic particle swarm optimization for enhancing energetic and exergetic performances of hydrogen energy production from plastic waste gasification

In recent decades, there has been a surge in demand for the development of renewable energies, leading to extensive research efforts focused on the gasification process. Consequently, a wide range of studies has been conducted to explore different feedstocks for gasification, with particular emphasis on plastic waste. Although a wide range of studies conducted various optimization methods for the gasification process, there was no specific study to employ metaheuristic methods in the field of plastic waste gasification. This study aimed to perform a multi-objective particle swarm optimization (MOPSO) method to solve a multi-objective plastic waste gasification optimization problem for enhancing energetic and exergetic viewpoints. Moreover, exact methods including non-linear optimization and response surface methodology (RSM) were conducted to be compared with the results derived from MOPSO. Pareto-front solutions obtained from MOPSO were ranked using grey relational analysis (GRA) and the optimum responses were 517 kJ/kg, 84 %, and 57 % for lower-heating value, cold gas efficiency, and exergy efficiency, respectively, which were compared to optimum options obtained from multi-objective non-linear optimization and RSM techniques. While the MOPSO model may not have performed as well as other methods in this particular problem, it is important to acknowledge its capabilities. Future research can concentrate on improving the behavior and performance of the MOPSO method when dealing with a larger number of points or when applied to different problem domains.

An Intelligent Food Recommendation System for Dine-in Customers with Non-Communicable Diseases History

The rising prevalence of diet-related diseases necessitates a focus on individual food selection to enhance nutrition intake and promote overall health. This study introduces a novel food recommender system utilizing artificial intelligence, specifically a genetic algorithm (GA), to intelligently match diverse nutritional needs with available food items. The research incorporates machine learning methodologies, such as collaborative and content-based filtering, to develop a recommendation model. Data from a commercial restaurant, Nutrisurvey, and the Indonesian food composition list inform the nutritional analysis of five menu items. Consumer variability, considering factors like sex, body mass index, medical conditions, and physical activity, are integrated into the GA framework for personalized food pattern matching. The presented results demonstrate the efficacy of the proposed model in offering tailored food recommendations for consumers with non-communicable diseases (NCDs), such as diabetes, hypertension, and heart disease. The multi-objective optimization technique employed in the system ensures a balance between nutritional adequacy and individual preferences. The presented GA-based approach holds promise for promoting healthier food choices tailored to individual needs, contributing to the broader goal of fostering a sustainable and personalized food system.

Optimization of Problems with Multi-Objective Functions and their Applications in Engineering

Many real-world optimization issues typically have multiple competing goals. There is generally no solution in those multi-objective optimization problems that optimizes all objective functions at the same time. Rather, "efficient" in terms of all objective function’s solutions known as Pareto optimum solutions are presented. We typically have a large number of Pareto-optimal options. As a result, we must choose a final solution from among Pareto optimal solutions while considering the objective function balance; this process is known as "trade-off analysis." It is not hyperbole to state that trade-off analysis is the most crucial task in multi-objective optimization. As a result, the methodology's ease of use and comprehension for trade-off analysis should be highlighted. The set of Pareto optimal solutions in the objective function space, or Pareto frontier, can be represented somewhat simply in circumstances when there are two or three objective functions. We can fully understand the trade-off relationship between objectives when we see Pareto boundaries. Thus, in scenarios with two or three objectives, it would be the most appropriate technique to represent Pareto borders. Reading the trade-off relationship between objectives with three dimensions, however, may be challenging. Nonetheless, Pareto frontier cannot be represented in situations where there are more than three objectives. In this case, interactive techniques can assist us in performing a local trade-off analysis that reveals a "certain" Pareto optimal option. Many techniques have been devised, with variations in which the Pareto optimal solution is displayed. The main concerns of such multi-objective optimization techniques, especially as they relate to engineering design problems, are covered in this study.

Achieving green mobility: Multi-objective optimization for sustainable electric vehicle charging

This study optimizes and evaluates a Photovoltaic-Wind-Battery/Electric Vehicle Charging Station (PVWB/EVCS) system using four Multi-Objective Optimization (MOO) techniques: MOPSO, NSGAII, NSGAIII, and MOEA/D. The main goals are to minimize the Total Net Present Cost (TNPC) and Loss of Power Supply Probability (LPSP) of the system, which are crucial for sustainable electric vehicle charging. The study analyzes the economic, operational, and sustainability aspects of the optimized system and compares it with HOMER software. The results show that NSGA-II is the best MOO technique for this problem, as it has the best performance and robustness. The Discounted Cash Flow analysis confirms the economic feasibility and sustainability of the optimized system over its lifetime. The technical analysis demonstrates the system's ability to use renewable energy from solar and wind sources, along with efficient energy storage and distribution. The study also conducts a sensitivity analysis to investigate the effects of changes in load, irradiance, wind speed, and component costs on the system performance. The findings reveal the system's resilience and adaptability under different scenarios, thus enhancing its suitability for renewable energy generation and electric vehicle charging. The study showed that the optimized PVWB/EVCS system is a promising solution for reducing reliance on non-renewable sources and promoting a more eco-friendly sustainable future.

Integrated optimization of common rail direct injection diesel engine input parameters with linseed biodiesel: A hybrid approach using grey relational analysis and genetic algorithm techniques

In the pursuit of improved productivity and efficiency, the search for alternative fuels that reduce dependence on fossil fuels is of utmost importance. This research study aims to investigate the influence of process parameters, namely blend percentage, fuel injection pressure, exhaust gas recirculation (EGR) rate, and engine load, on various engine performance parameters such as torque, brake thermal efficiency (BTE), brake mean effective pressure (BMEP), hydrocarbon emissions (HC), and mechanical efficiency. The experiments are conducted following the Taguchi orthogonal array (L25) design of experiments. Additionally, grey relational analysis, a multi-objective optimization technique, is applied to identify the optimal levels of the process variables that optimize all the response parameters. The optimal values obtained through grey relational analysis are determined as 0% blend, 600 bar fuel injection pressure, 12% EGR rate, and 12 kg engine load. Moreover, the Genetic Algorithm-based Multi-Objective Genetic Algorithm (MOGA) is employed for multi-objective optimization of the engine input variables, yielding optimal levels of 19.45% blend, 594.35 bar fuel injection pressure, 14.12% EGR rate, and 12 kg engine load. Furthermore, multivariable regression models are developed to predict the response variables within the experimental domain. These models are validated through a confirmation test. The findings of this study provide insights into the optimization of process variables for enhanced engine performance, with the developed models serving as valuable tools for future predictions and optimization.

A two-level optimisation approach for underground natural ventilation based on CFD and building energy simulations

Natural ventilation is a historical and promising passive design for conserving building energy. However, a noticeable scarcity exists in design strategies and academic studies on underground natural ventilation. This study introduces a two-level optimisation approach for underground natural ventilation, considering form and environmental characteristics. At the initial level, computational fluid dynamics simulations were employed to quantify and visualise the momentary ventilation performances of various passive strategies. These simulations also furnished precise wind pressure coefficients. Subsequently, at the second level, this study predicted the annual energy savings of the proposed prototype by integrating an airflow network model with building energy simulations. To adapt design parameters, sensitivity analysis and multi-objective optimisation techniques were applied. These numerical models underwent validation against measured data from analogous studies. The results reveal that the ‘inlet buried pipe + outlet shaft’ led to a 6.9 % reduction in energy consumption for heating, ventilation, and air-conditioning in underground hybrid ventilation compared to underground mechanical ventilation. Moreover, it achieved a 28.8 % decrease compared to aboveground mechanical ventilation. Optimising air temperatures following climate, soil conditions, and building characteristics is imperative, aligning with design standards. This study contributes a quantitative and visual approach to support underground natural ventilation and addresses the limitations of empirical designs, providing a foundation for future research and practical applications.

Modelling and Optimisation of Cooling-slope Parameters of Magnesium AZ91D using Improvement Multi-Objective Jaya Approach for Predicted Feedstock Performance

The cooling-slope (CS) casting technique is one of the simple semi-solid processing (SSP) processes a foundryman uses to produce the feedstock. This study attempts to develop mathematical regression models and optimise the CS parameters process for predicting optimal feedstock performance, which utilises tensile strength and impact strength to reduce the number of experimental runs and material wastage. This study considers several parameters, including pouring temperature, pouring distance, and slanting angles for producing quality feedstock. Hence, multi-objective optimisation (MOO) techniques using computational approaches utilised alongside the caster while deciding to design are applied to help produce faster and more accurate output. The experiment was performed based on the full factorial design (FFD). Then, mathematical regression models were developed from the data obtained and implemented as an objective function equation in the MOO optimisation process. In this study, MOO named multi-objective Jaya (MOJaya) was improved in terms of hybrid MOJaya and inertia weight with archive K-Nearest Neighbor (MOiJaya-aKNN) algorithm. The proposed algorithm was improved in terms of the search process and archive selection to achieve a better feedstock performance through the CS. The study’s findings showed that the values of tensile and impact strengths from MOiJaya_aKNN are close to the experiment values. The results show that the hybrid MOJaya has improved the prediction of feedstock using optimal CS parameters.