Multi-objective orthogonal opposition-based crow search algorithm for large-scale multi-objective optimization

Many real-world multi-objective optimization problems have hundreds or even thousands of decision variables, which contrast with the current practice of multi-objective metaheuristics whose performance is typically assessed using benchmark problems with a relatively low number of decision variables (normally, no more than 30). In this paper, we propose a cooperative coevolution framework that is capable of optimizing large scale (in decision variable space) multi-objective optimization problems. We adopt a benchmark that is scalable in the number of decision variables (the ZDT test suite) and compare our proposed algorithm with respect to two state-of-the-art multi-objective evolutionary algorithms (GDE3 and NSGA-II) when using a large number of decision variables (from 200 up to 5000). The results clearly indicate that our proposed approach is effective as well as efficient for solving large scale multi-objective optimization problems.

Acceptance of new students is a school activity that occurs every year, even in the middle of the teaching year. This activity is the first step to improve the quality of education, namely choosing the quality of students. In general, the process of admitting new students is still done manually, namely by recording student data and the student selection exam schedule to the student data that has been accepted. This is one of the problems every year that can hinder the work of teachers and even school staff. Therefore, we need the right system to be used in schools so that it can assist in the acceptance of new students. One system that is able to solve these problems is the Decision Support System (SPK). . SPK aims to provide information, guide, provide predictions and direct users of information so they can make better decisions. In implementing the SPK, ranking methods are used so that the results obtained are more effective, such as the Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) Method. The Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) method where it is expected that the normalization process in calculating the value of new student admissions. The Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) method is a method which has a calculation level using minimum calculations and is very simple and practical for applied to the system. The Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) method has a high level of flexibility and convenience on separating the subjective part that comes from an assessment process into the decision weighting criteria using several decision-making attributes. The final result showed that Angga Rifai Zul Fiqri ranked first with a final preference score of 1.0522, which was the highest among all candidates.

A fast sampling based evolutionary algorithm for million-dimensional multiobjective optimization

Multi-objective evolutionary algorithms (MOEAs) of the state of the art are created with the only purpose of dealing with the number of objective functions in a multi-objective optimization problem (MOP) and treat the decision variables of a MOP as a whole. However, when dealing with MOPs with a large number of decision variables (more than 100) their efficacy decreases as the number of decision variables of the MOP increases. On the other hand, problem decomposition, in terms of decision variables, has been found to be extremely efficient and effective for solving large scale optimization problems. Nevertheless, most of the currently available approaches for large scale optimization rely on models based on cooperative coevolution or linkage learning methods that use multiple subpopulations or preliminary analysis, respectively, which is computationally expensive (in terms of function evaluations) when used within MOEAs. In this work, we study the effect of what we call operational decomposition, which is a novel framework based on coevolutionary concepts to apply MOEAs’s crossover operator without adding any extra cost. We investigate the improvements that NSGA-III can achieve when combined with our proposed coevolutionary operators. This new scheme is capable of improving efficiency of a MOEA when dealing with large scale MOPs having from 200 up to 1200 decision variables.

The selection process for prospective cadets at a maritime college is routinely carried out every year. In the current system, the processing and calculation of prospective cadet scores uses Microsoft Excel tools, but this takes a long time and can lead to errors in the calculation of scores and the recapitulation of selection results. In addition, unintegrated data can lead to data duplication, resulting in inaccurate selection results that can be detrimental to prospective cadets. A fast, precise, and accurate integrated decision support system is needed to support the selection process for prospective cadets. The author conducted a study on the prospective cadet admission system at UNIMAR AMNI Semarang using the Multi-Objective Optimization On The Basis Of Ratio Analysis (MOORA) method to assist in the selection of qualified prospective cadets who meet the established criteria. With this decision support system using the Multi-Objective Optimization On The Basis Of Ratio Analysis (MOORA) method, it can be used to solve the selection problem for prospective cadets because it has a short calculation time, is simple, transparent, and has high flexibility. This system will be built using the Codeigniter Framework and a MySQL database.

One of the most significant factors which affects the success of ERP implementation is to determine the proper software. To successfully implement an ERP project, it is a necessity to determine the proper software compatible with the structure of the enterprise. The implementation of an improper software causes a great amount of loss of funds and time as well as a disruption of activities. This study, through the usage of fuzzy AHP (Analytic Hierarchy Process) and fuzzy MOORA (multi-objective optimization on the basis of ratio analysis) methods, aims to suggest some solutions on determining the most proper software based on the expectations and needs of enterprises active in manufacturing sector. Within the study, Fuzzy AHP and fuzzy MOORA methods are used to determine the most proper ERP software for manufacturing sector. Within this context, as the first step the factors whose reliability has been tested in the literature have been determined and then a survey has been conducted on the decision-makers. With Decision support has been provided for the executives with the decision matrix formed in accordance with executives’ opinions, by using fuzzy AHP method to determine the criteria weight and fuzzy MOORA method to evaluate the alternatives.

Water is essential for life, as it supports bodily functions, nourishes crops, and maintains ecosystems. Drinking water is crucial for maintaining good health and can also contribute to economic development by reducing health-care costs and improving productivity. The present study evaluated the surface water quality of Mahanadi River (Odisha, India). Hence, to evaluate the hydro-chemical processes, sources of contamination, and water quality, a methodical examination was conducted using an integrated approach, namely Bayesian Approximation (BA), Analytical Hierarchy Process (AHP)-Technique of Order of Preference by Similarity to Ideal Solution (TOPSIS), Fuzzy Logic (FL), Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA), and Random Forest (RF) method. For this, water samples from 16 locations were taken for a period of 2018–2024, to test 21 physicochemical parameters in the selected sampling sites. From the assessment of parameters, with respect to WHO standards, pH indicates alkaline, TKN, and TC in all samples surpassed the prescribed drinking water limit. However, major ion and hardness spatial interpolation maps typically show that the quality of the water declines from upstream to downstream, with extreme values found in the downstream. The index for BA-WQI value revealed that 50% of samples belong to unsatisfactory water quality. This was also accompanied by several parameter’s high values, namely TDS, NO3−, Cl−, and SO42−, which were also highest among all the locations. Again, it is noticed that 12.50% of sites come under the zone of excellent water. However, 37.50% of samples indicated a good water class. As a result, a renowned MCDM model, such as AHP-TOPSIS, was presented, which makes use of rough set theory and Bayesian weights to provide a trustworthy and objective assessment of the total pollution levels at each sample site. Hence, this innovative technique depicted that W-(9) was the most polluted region if compared to other places, followed by W-(8), (16), (2), and (7), respectively. Based on FL-DWQI values, 12.5% of monitored specimens point towards excellent category, and rest 18.75% indicated as good quality. The remaining samples, or 68.75%, consist of ‘poor, very poor, and unsuitable qualities'. However, it was relevant that the degree of pollution at these stations was more closely linked to a variety of expanding human activities, such as excessive water use, fertilizer effects, agricultural runoff, and industrial activity in and around the river corridor. Additionally, MOORA has been conducted and performance scores were extracted. These four polluted sites such as W-(9), (8), (16), and (4), which contain higher performance scores, were 0.89, 0.093, 0.06, and 0.04. The major four places containing variables that exceeded the WHO limits, which account for TKN, coliform, and EC properties, were named accordingly. It was discovered that the main causes of the river’s water quality adulteration were agricultural runoff and home waste water. Furthermore, a RF analysis of the 16 sites was carried out and five critical variables such as Cl−, TH, TDS, EC, and TC were obtained on the basis of R2 and RMSE score. Here, the first four RF factors were sufficient to explain 83.86%, 84.27%, 84.14%, and 85% of the model accuracy for correlation matrix. In the end, the target TC suggests about 89% of the total accuracy. Afterwards, water quality at all sampling locations was expressed in terms of RF-WQI. The value obtained varied between 15 and 243, denoting good to poor water. The main finding of the investigation was that at eight inadequate water sites, the main sources of adulteration of the river’s water quality were agricultural runoff, illegally deposited municipal solid waste, and deteriorating household water supplies. This work highlights the viability and dependability of integrating these techniques for monitoring and evaluating river water quality. Hence, these findings are essential for comprehending surface water sustainability, for human consumption in the research area.

Hospital is a comprehensive part of a social and medical organization, which has the function of providing health services to the community both curative and preventive, where the services provided are inpatient, outpatient, and emergency units. Health is the most important thing that every human being wants to survive in doing all activities. The importance of health encourages the government and the private sector to build hospitals so that people can provide health needs. To find out which hospital has the provision of health services desired by the community, hospital ranking is conducted. Hospital ranking is done by looking at the service time for each process in the hospital. The method used by researchers is the method of Analytic Hierarchy Process (AHP) and Multi Objective Optimization with Basic Ratio Analysis (MOORA). AHP method is selected because it is able to select alternatives from several alternatives based on predetermined criteria. The results of this study are the average values of each alternative and the criteria that will be used as the best honed hospital ranking. Based on the experiment, it is found that Hospital 8 is ranked 1. Hospital 7 is ranked 2. and Hospital 2 is ranked 3.

Insurance product offerings are not always understood by prospective customers (CN) due to limited information related to products. This can cause confusion so that CN does not want to buy it. The purpose of this study is to analyze the selection of insurance products PT. AIA Financial Samarinda, East Kalimantan, Indonesia uses the Analytical Hierarchy Process (AHP) and Multi Objective Optimization on the Basis of Ratio Analysis (MOORA) approach so that CNs can choose based on insurance product facilities that match their abilities. In this study, as many as 10 types of insurance products and 10 CN criteria were then analyzed based on the two methods used. Then, the calculation accuracy of the two methods has been using the confusion matrix (CM) method. Based on the results of CM calculations from 27 CN datasets with a conformity level of 81.5%, it has been obtained which indicates that the two methods can be implemented as an alternative in choosing insurance products according to ability or based on CN criteria. The results show that this method is quite effective, efficient and relatively easy to use in determining insurance products that meet the criteria or according to CN's economic capabilities

This paper presents a random-based dynamic grouping strategy (RDG) for cooperative coevolution to deal with large scale multi-objective optimization problems (MOPs) by decomposing the whole dimension into several groups of variables with an equal size. First, a decomposer pool containing different group sizes is designed. Then, a group size is dynamically selected with probability in the evolution process. The probability of each group size in the pool is computed based on the historical performance measured by C-metric, a common metric in multi-objective optimization. Under the selected group size, random grouping is executed to decompose the whole dimension into groups. Through this, both the group size and the group components are dynamic. Finally, combining RDG with a traditional and famous multi-objective evolutionary algorithm (MOEA) named MOEA/D, we develop MOEA/D-RDG to cope with large scale MOPs. The efficacy of the proposed MOEA/D-RDG is verified on two sets of MOPs (UF1-UF10 and WFG1-WFG9) through comparing with two MOEA/D variants.

This study aims to assist wood craftsmen in Dongkelan, Krapyak, Yogyakarta in determining the best wood to be used as guitar material, because there are frequent complaints from buyers that the materials used as guitar materials are rotten quickly and are dull in terms of color. Based on these problems, a solution is sought using the Multi Objective Optimization on the basis of Ratio Analysis (MOORA) decision support system method, and is assisted by experts in determining the right criteria related to determining the best wood used in making guitar materials, after a long time discussing the correct criteria were found based on the problem, in the form of criteria for wood strength, wood grain, texture, and wood weight. All of these criteria are then processed using the MOORA decision support system method. After processing, the best results are obtained. The right wood for guitar making is ebony with 23.6831 results occupying the first rank. Proving the results of the MOORA decision support system method, a questionnaire was carried out directly to several guitar makers with a total of 14 people, resulting in an accuracy of 85.71% which means that it has significant verification, that ebony wood is best used as a guitar-making material

This research aims to develop and implement a Decision Support System (DSS) using the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method to determine the best employee at Hotel Kristal Jakarta. The main problem faced by the hotel is that employee performance assessments are still conducted subjectively, time-consuming, and prone to bias. The MOORA method is chosen because it can handle multi criteria decision making systematically through normalization of the decision matrix and the calculation of the Yi index by combining both benefit and cost criteria. The research begins with the identification of evaluation criteria: knowledge, responsibility, honesty, work productivity, and attendance. Based on interviews with the HRD Director and a literature review. Each criterion is then according to its level of importance. An employee database was built to store the scores for each criterion. The MOORA computation process is implemented using PHP, with a Bootstrap 5-based interface, allowing users (HR admins) to input data, perform automated calculations, and view ranking results in real time. System testing was conducted using data from 15 employees for the May 2025 period. The results show that the system successfully generates employee rankings objectively, the highest Yi value is assigned to the best employee, namely Edy Sarwoko (A6), with a Yi score of 0.218. The implementation of the MOORA-based DSS significantly improves the efficiency of employee evaluation and reduces assessor bias.

Abstract. The selection of optimal plastic pellet samples is often constrained by subjective manual assessments, affecting product quality and production efficiency. This study presents a web-based Decision Support System (DSS) utilizing the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method to improve objectivity in the selection process. Five weighted criteria are used: material cleanliness, physical quality, material strength, price, and plastic type suitability. MOORA calculates the optimization value for each alternative, enabling precise and accountable recommendations. The system’s implementation demonstrates enhanced selection speed, reduced subjectivity, and support for quality-based pricing decisions. Overall, the proposed DSS effectively improves decision-making accuracy and contributes to better production outcomes.

Warehouse staff is a job related to the storage of goods for production or production. Therefore, the person who occupies the position is required to have certain criteria. However, during this time, the warehouse head of PT. Royal Abadi Sejahtera has experienced difficulties in the process of accepting warehouse staff, because there are many applicants who come to apply and do not meet the expected criteria. The problem faced in this research is that the company is still doing the selection by looking at prospective staff willing to work, the work does not only require a level of willingness to work, but has skills, education, work experience, attitude in work and good communication. Decision Support System is a system that is used to assist in the decision-making process in an organization. In the process the Decision Support System uses several methods used in the decision-making process and one of them is the MOORA (Multi Objective Optimization on the Basis of Ratio Analysis) method and the ROC (Rank Order Centroid) method for the weighting process. The benefits that the author wants to achieve in this study are to assist the company in facilitating the process of accepting warehouse staff as input for companies in making decisions using a decision support system to provide a more objective assessment resulting from the Decision Support System.

This paper delves into the development and validation of the RAMDOE method, a pioneering approach in multi-criteria decision making (MCDM) that seamlessly integrates the root assessment method (RAM) and design of experiments (DOE) techniques, addressing the inflexibility of traditional MCDM methods in accommodating adjustments in criteria ranges and the addition of new alternatives without necessitating a complete overhaul of the decision framework. Through empirical analysis, the study demonstrates the RAMDOE method's remarkable efficacy in precisely ranking alternatives, as illustrated through a practical case study focused on the selection of a supplier from a pool of seven candidates. One of the most notable aspects of the RAMDOE method lies in its capacity to formulate a regression equation that accurately captures the intricate relationship between alternative scores and criteria values, enabling decision-makers to seamlessly integrate new alternatives into the decision-making process without the cumbersome task of recalibration, thereby distinguishing it from conventional MCDM techniques such as TOPSIS (technique for order of preference by similarity to ideal solution), COPRAS (complex proportional assessment), MOORA (multiobjective optimization on the basis of ratio analysis), EDAS (evaluation based on distance from average solution) and CODAS (combinative distance-based assessment). The practical implications of these findings are profound, offering decision-makers across various domains a more efficient and adaptable framework to navigate complex decision scenarios. Particularly in contexts like supplier selection, where criteria ranges may vary significantly, the RAMDOE method provides decision-makers with a robust toolset to make informed decisions, presenting a promising avenue for addressing the dynamic nature of decision-making environments and enhancing the overall robustness and flexibility of MCDM processes in real-world applications