Software performance engineering in early life cycle of software development (software modeling) is very useful and cost effective, but not fully automated yet. This paper presents an optimization method based on multi-objective particle swarm optimization (MOPSO) for exploring the software design space automatically and proposes the best configuration in terms of performance evaluation. The software model is transformed in to a performance model, which is based on Layered Queueing Networks (LQNs). Then, the model will be optimized and feedback to the software model. One case study is utilized for evaluating the proposed method. The results obtained apparently show the optimization performance of MOPSO.

Numerous problems encountered in real life cannot be actually formulated as a single objective problem; hence the requirement of Multi-Objective Optimization (MOO) had arisen several years ago. Due to the complexities in such type of problems powerful heuristic techniques were needed, which has been strongly satisfied by Swarm Intelligence (SI) techniques. Particle Swarm Optimization (PSO) has been established in 1995 and became a very mature and most popular domain in SI. Multi-Objective PSO (MOPSO) established in 1999, has become an emerging field for solving MOOs with a large number of extensive literature, software, variants, codes and applications. This paper reviews all the applications of MOPSO in miscellaneous areas followed by the study on MOPSO variants in our next publication. An introduction to the key concepts in MOO is followed by the main body of review containing survey of existing work, organized by application area along with their multiple objectives, variants and further categorized variants.

The presence of distributed generations (DGs) in the power systems is causing problems such as increasing the short circuit current levels which may exceed the rating of existing circuit breakers and can damage system equipment. The utilization of fault current limiters (FCLs) in the network can be an effective method to overcome the above problems. Furthermore، FCL has the benefits such as improving the system security and reliability. FCL benefits depend on the number، installation location، and impedance of FCL. For this end، we require a method to determine the optimum number، impedance and locations for FCL placement. In the considered method، we have modeled the FCL placement as an optimization problem while the objectives are; bus fault current difference، reliability، the number and impedance of FCLs. Moreover، to solve the proposed problem، a new multi-objective optimization algorithm based on particle swarm optimization has been implemented. In the algorithm، several iterations have been considered، and the non-dominated solutions are extracted and stored in an external repository in the iterations. Finally، a fuzzy clustering technique is used to control the size of the repository during the algorithm evolution. The proposed approach is tested on a test system، namely، RBTS 2. The obtained results demonstrate the effectiveness and feasibility of the new method.

Introduction With the developments in navigation, positioning, and tracking technologies, a large amount of moving point data (e.g., human, vehicle, animal) have been produced. Through moving an object in the course of time, a sequence of its position is recorded which is known as trajectory. Studying the behaviors of point objects and analyzing their trajectories have recently received great attentions among researchers in different fields of science, especially in geographic information science. Such studies contribute to better understanding of movement-behavior patterns of moving objects. Data mining, as one of the main approaches in geographic knowledge discovery, is normally used in moving databases to extract information from moving point objects’ trajectories. Analyzing the similarity of trajectories as one of the frequently used approaches in geographic data mining, is of great importance, which is normally performed by distance functions.

Many researchers have applied single-objective optimization algorithms for structural damage identification. Recently, multi-objective optimization algorithms have also been considered for this purpose. This article applies two recent multi-objective evolutionary optimization algorithms and modal shape and stiffness objective functions for structural damage detection in a simply supported beam and a two fixed-end moment beam. For the fixed supported beam, the results are compared with the experimental results from the literature. The results demonstrate the good performance of proposed approach in localization and quantification of damages.

Throughout the study, a method is proposed by making use of a multi-objective structure and employing new formulations, where instead of increasing reliability based on meeting a demand of 100 percent in some months regardless of the dry months, part of the water of wet months or wet seasons be stored in reservoirs to be used in dry months to compensate for failure intensity. To this end, Multi-Objective Particle Swarm Optimization (MOPSO) algorithm was connected to the WEAP simulation model. The main purpose of this type of structures is to offer a resolution to increase the percentage of demand coverage in dry months in addition to reach an acceptable demand meeting reliability over the entire period depending upon the operation capacity of the reservoir. Ultimately, the results of three scenarios, including a current situation, land development management scenario and an optimization one, were evaluated. According to the results of the current situation scenario, in all the operation period the situation was reported acceptable, except for a few months. In land development scenario, for most consumptions in most of the dry years and in the last six years of planning, the demand coverage was equal to zero in three to eight consecutive dry months, and it was lower than 5% in these months in the rest of the low-water years. On the other hand, the demand coverage increased from 28% to 60% in these months by implementing the optimization model. Also, in the optimal scenario of reliability, supplying downstream environmental demand as well as the Maroon hydroelectric dam need was improved. This study depicts that using the strategies of this research will lead to a better reservoir management and will reduce failure intensity in supplying different consumptions during low-water months.

The dramatic increase in number and turnover of the projects of organizations on one hand and the Aggravation of environmental concerns on the other hand, lead to Increasing attention to environmental concerns in the field of project management. Adding this factor to the other customary factors that have an impact on project scheduling is a reasonable approach toward evaluation and control of destructive environmental effects. To this end, environmental impacts have been considered as a novel factor in the time-cost trade off problem and a new mathematical model, which includes time, cost and environmental impacts simultaneously, has been proposed in this article. Due to its NP-hardness, two metaheuristic algorithms, namely MOPSO and MOFA, combined with a heuristic algorithm were coded in MATLAB software. The heuristic algorithm’ s function is to transform infeasible solutions to feasible ones. The results of implementing the aforementioned model and algorithms in a drilling project indicate that project managers can choose between different amounts of time, cost and environmental impacts. Moreover, they can control environmental impacts of a given project as well. Furthermore, the values of Pareto answers criteria demonstrated that MOPSO algorithm outperforms MOFA algorithm in this project.

Given the increasing demand for electric vehicles, optimizing their structural design is a key engineering challenge. In this research, with the aim of achieving an optimal design for the body structure of an L6e class electric vehicle, the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm has been utilized. In this optimization problem, the main objective is to increase the torsional and bending stiffness of the vehicle's body structure. The dimensions of the structural beams were considered as design variables. Since the complete body structure model in Catia was not suitable for optimization due to the multiplicity of design variables, especially in connections, a simplified model was first created in Abaqus software. This model was constructed using beam elements and rigid connections to facilitate the optimization process. To validate the reliability of the simplified model, torsion and bending tests were performed on both the simplified Abaqus model and the complete Catia model, showing that the difference in stiffness was less than 4%. This result fully confirmed the reliability of the simplified model for use in the optimization process. Finally, Abaqus scripting capability allowed for the parameterization of profile dimensions, which enabled the automatic link of the model to the MOPSO optimization code in Matlab and the generation of new structures. The results of this research showed that by utilizing MOPSO multi-objective optimization, a significant simultaneous increase in the torsional stiffness (32.10%) and bending stiffness (33.79%) of the structure could be achieved, compared to the initial model before optimization.

Almost all real-world decision-making issues, especially in the water resource management area, are multi-objective issues that incorporate different and conflicting objectives. Due to the wide range of such applications, different models have been proposed to tackle multi-objective optimizations, among them NSGA-II and MOPSO are the most important models. The purpose of this study is to compare the performance of NSGA-II and MOPSO algorithms in solving multi-objective optimal operation of a hydropower reservoir. The results showed that by reducing the minimum daily energy production from about 1040 MW to 650 MW, we will meet an increase in the revenue of about 10% of the total annual revenue.

Monitoring network optimization is a decision making process for the best combination of available stations. Due to economic considerations and reduction of monitoring costs, the optimization approach in this study is to reduce monitoring stations without reducing the amount and accuracy of the information obtained. In this study, an optimal design of groundwater quality monitoring network was carried out with the help of an optimization model in the Neishabour plain aquifer. The optimization of the wells network was accomplished by a Multi Objective Particle Swarm Optimization (MOPSO) algorithm. Two objectives containing of minimizing the root mean square error (RMSE) and the number of wells was applied in current research. Kriging interpolation was used for calculating groundwater chlorine concentration values and compared with observation values. As a result of this research was presented a Pareto front exctracted from MOPSO showing the number of wells against their corresponding RMSE, which could be a guide for the design of a groundwater quality monitoring network. The outcome showed that the sampling wells can be reduced to 58 percent with a minimum error increase (all 50 wells in base network with zero error may be reduced to 21 with chlorine concentration error of 13. 57 mg/l) in the Neishabour aquifer. Also, the position of these wells was considered as the optimal position.