multi-objective optimization

The emergence of the Internet of Things (IoT) has intensified the focus on web service composition and the fulfillment of increasingly complex and diverse user requirements. IoT-based systems often encounter numerous service candidates with varying qualitative attributes, presenting a significant challenge in selecting an optimal combination. This problem, categorized as NP-hard, requires efficient approaches for resolution. This study proposes a near-optimal solution for web service composition in IoT environments by leveraging the NSGA-III multi-objective metaheuristic algorithm to identify the optimal Pareto front. To further enhance the quality and diversity of the solutions, an improved algorithm integrating NSGA-III with a novel fitness function is introduced. The proposed approach optimizes service composition using nine quality parameters, which are subsequently streamlined into three principal objectives for better computational efficiency. Experimental evaluations demonstrate that the proposed method outperforms the baseline NSGA-III algorithm in terms of the average performance of two out of three objectives. Additionally, the approach achieves an average of 11% higher coverage based on performance indices and exhibits superior solution distribution and dispersion compared to alternative algorithms.

Software-Defined Networking (SDN) has been recognized as an efficient approach in the field of communication technology, aiming to improve the performance and efficiency of computer networks, thus reducing costs. One of the key challenges in SDN is load balancing among nodes. Solving this challenge leads to improved response time and network performance. Nowadays, various methods have been proposed for load balancing in SDN, but they have not yet reached the ideal state. In this article, a new method is presented to enhance load balancing and reduce response time. This method utilizes multi-objective evolutionary algorithms and fuzzy weighting. In the proposed method, factors such as bandwidth, traffic status, link buffer, and desired router are taken into account, and the best path and router with desired load balancing for information flows are selected with the minimum time. One prominent advantage of this method is the possibility of performing load balancing automatically without the need for human intervention. Experimental results demonstrate that the proposed method shows a significant improvement of approximately 14.8% in response time compared to other methods, while maintaining load balancing in SDNs. By using the proposed method, in addition to improving service quality and user satisfaction, response time will also be enhanced. In summary, the proposed method is introduced as a viable approach in SDNs and exhibits superiority over existing methods.

Considering the variety of urban buildings from the point of view of regularity and irregularity, it is important to examine their life cycle cost (LCC); this issue has not yet been fully explored in previous research. On the one hand, the lack of utilization of building information modeling (BIM) in previous structural design-based LCC research is evident. This research aims to highlight the impact of irregularity on the LCC of structures by providing an integrated framework based on the seismic design optimization of structures by using LCC and BIM capacities. For this, a shared environment is created in MATLAB software, information is exchanged between Revit, Etabs, and Excel software, and optimization is done using NSGA-II for establishing a trade-off between initial cost and LCC. BIM tools can greatly reduce the limitations of LCC analysis, such as information exchange time, and increase the accuracy and speed of calculations. By modeling six models in two regular and irregular types, the framework of the research and the difference in the behavior of the structures are examined. The results showed that the indirect costs of irregular structures are more than the regular ones. In addition, the findings show that reducing the LCC of irregular structures compared to regular ones requires a higher initial cost percentage. For example, for regular and irregular 13-story structures, a 17% increase in the initial cost leads to a 48% and 40% reduction in their LCC, respectively.

Considering that dams are one of the most important water storage resources in water projects, therefore, proper construction and operation management of them is one of the most important responsibilities of water resource managers. One of the important parts in the construction of any dam is the design of the dam spillway, which is responsible for discharging excess water from the reservoir to downstream in case of floods. Destruction or inefficiency of spillways causes severe damage to the dam or even causes its complete destruction, that is why the design and construction of this part of the dam is very important. Among different types of spillways, stepped spillways, in addition to passing excess water from the reservoir of dams, are also a structure that consumes flow energy. Until now, in the studies carried out in this field, the optimization of stepped spillways has been considered as a single objective, which usually aims to optimize, minimize construction costs or maximize energy consumption in these spillways, and rarely optimize the dimensions of spillways with Taking into account the two factors of cost and energy consumption has been considered simultaneously. Therefore, in this study, for the first time, a multi-objective optimization model has been developed by benefiting from the capabilities of the improved version of the dragonfly optimization algorithm for the design of stair overflows with the aim of minimizing the volume of concrete used and increasing energy consumption.

In the design of the stepped spillway, the discharge parameters (Q) and the horizontal length of the overflow (L) are among the most important input parameters, which are determined based on the initial studies of topography and hydrology and are considered constant during the design. According to the purpose of the present research, an optimal combination of effective parameters in the design of a stepped spillway, including slope, width, height of the spillway, water pressure on the spillway, and step height, should be determined in a way that leads to the highest energy consumption and the lowest design costs with By-law restrictions should be taken into account. In the current research, the objective function, which consists of decision variables, should simultaneously provide the maximum amount of energy consumption and the minimum volume of consumed concrete, while satisfying the constraints and hydraulic conditions of the design problem. For this purpose, the dragonfly optimization algorithm, which is one of the powerful algorithms of collective intelligence methods, and was first proposed by Mirjalili.This algorithm is inspired by the group and social behavior of dragonflies in nature. Dragonflies usually live alone in the wild and only act in groups for hunting and migration. The basis of the dragonfly optimization algorithm to find the optimal answer is their two crowding behaviors. In this way, based on the static behavior, the members of the algorithm population are divided into smaller groups and search the problem space, then based on their dynamic behavior (exploitation phase), they determine the optimal answer with more members.

The spillway of Sarouq Dam, located in West Azarbaijan province, was selected as a case study in this research to evaluate the developed model. In the first part, the problem of optimal design of the stepped spillway of Sarouk Dam was investigated as a single-objective problem with the aim of maximizing the amount of energy consumption. The purpose of redesigning Sarouk dam spillway was to provide an optimal design for a stepped spillway instead of the existing flat spillway with the aim of maximizing energy consumption downstream of the dam. In all the optimal models provided by IDA, the consumption of water energy has increased significantly. According to the report of Hosseini, 2019, the amount of energy consumption at the foot of the existing spillway of Sarouk Dam is equal to 23.41, which the use of the stepped spillway in the present research increases it for the flow rates of 560.2, 776.9, and 422 cubic meters per second, respectively. 71.32, 65.72 and 74.74 percent. According to the main goal of this article, in this section, the new and improved algorithm based on the dragonfly algorithm has been used to optimize the multi-objective overflow of Sarouk Dam by considering the amount of concrete pouring and the amount of energy consumption as objective functions. The results shown that, using the multi-objective optimization approach for the design of stair overflows leads to the presentation of different types of designs with different implementation costs and energy consumption rates. Therefore, using the multi-objective optimization approach has led to the production of many answers, which allows the employer to design the overflow according to the budget he has.

In this research, using the improvement of a new metaheuristic algorithm called the dragonfly algorithm, a single-objective model was developed with the aim of maximizing energy consumption, and another multi-objective model was developed with the objectives of minimizing the construction costs of the stair overflow. The developed models were used for the optimal design of Sarouk Dam spillway. The use of the improved Dragonfly multi-objective optimization algorithm to minimize construction costs and increase energy consumption showed that the multi-objective optimization approach can provide a set of answers that is a great help to the employer in order to achieve an economic plan based on the budget. available

Faced with increasing threats and challenges of supply chains, it is necessary to redesign the supplier network based on hybrid approaches based on mathematical models and reliability. To solve the problem, this paper presents a new approach. Team formation, selection, design, and composition are still critical success or failure factors in any business within a company and organization. Criteria, parameters, various qualitative and quantitative methods, approaches, and techniques have been presented by several studies in TF so far. This study developed a hybrid approach to team formation (TF) and reliable supplier network design, focusing on a multi-objective model integrating fuzzy-set theory and social network analysis. Furthermore, this study addressed the relative importance value of precise relationships between members using fuzzy logic (expert workshop and fuzzy inference), backup team, capabilities (skills, expertise, or knowledge), capacity, and order allocation. It also carefully considers the relationships between team members using expert workshops and fuzzy inferences. Also, social network analysis metrics are used to suggest team leader(s). We used the augmented epsilon constraint (AUGMECON2) method to validate the model and solve small-scale problems with exact solutions. The model aimed to form a reliable team and supplier network with a maximum level of reliability, maximize the network weight of collaboration, and maximize the knowledge level of the main members( suppliers), simultaneously. The approach was evaluated through a numerical study of the actual data of the electro-optical camera for team formation, design and selection of a network of reliable suppliers, and order allocation. The results showed that the approach carefully selects the optimal supplier network and team based on all assumptions and suggests team leaders with social network analysis. One of the advantages of our model is simultaneously considering supplier network, reliability, FIS, and SNA in team formation. The use of uncertain data and combined methods and MADM for preselection can also be eective. The strategy of the optimal number of modules and product subsystems can also be included in the model. In future studies, other variables and parameters such as time, design phases, and the total cost can be considered. Also, because the problem is NP-hard; the use of meta-heuristic algorithms is suggested. Modeling a multi-product multi-period supply chain problem is suggested.