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.

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.

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.

In this work, we aim to determine the optimal performance characteristics of a solar tracking system in order to maximize the power generation through using the MOPSO Algorithm. Considering the sun path during a day, the necessity of using solar tracking systems to achieve the maximum power output from photovoltaic (PV) panels is investigated. The solar tracking system allows the PV arrays to follow sunlight all day long. The unidirectional tracking system follows the sun path, thereby, optimizing the angular motion of the PV arrays relative to the sun resulting in a higher power generation. In order to evaluate the performance of a PV system, the total solar radiation is calculated first for both the fixed and unidirectional tracking systems. Analyzing the results indicates that for June 20 th, the power generation of the PV module equipped with a unidirectional tracker is 35% higher than the fixed PV module. The optimal value of the declination angle, Azimuth, and arrays’ tilting angles in a unidirectional tracking system calculated using the MOPSO Algorithm are 31. 8° , 178. 2° and 85. 1° , respectively.

Background and Objective: Increasing concerns about energy and greenhouse gas emissions from fossil fuel consumption have encouraged many researchers to be involved in developing sources of renewable energies. Biodiesel derived from biomass can play a crucial role in this context. The main objective of this paper is to present a mathematical programming model for the biomass supply chain. Material and Methodology: Researcher through library research and preparing a questionnaire to estimate parameters and data associated with the uncertainty of parameters and then through interviews, expert opinions about the limits and changes to the decision-making parameters have collected. Then a fuzzy multi-objective mixed integer programming model is presented that model to minimize costs, minimize environmental impact and minimize the time of delivery of product in Biodiesel Supply Chain. Findings: After running the model, increasing objective function is to minimize the total cost, minimize environmental impact and minimizing the time the product reaches the customer contact temperature limits for different values were obtained. Discussion and Conclusion: In this study, the proposed mathematical programming model is solved with the MOPSO Algorithm. The results indicate the location and capacity of the facility, the amount of biodegradable and glycerin production, and the amount of extracted Jatropha oil and refined waste oils.

In supply chain management, the supplier's performance will be evaluated by several criteria. In this paper, a fuzzy multi-objective mathematical programming is designed to select the best suppliers as well order allocation with considering qualitative and quantitative factors as well as risk. At the first step, potential suppliers were evaluated by AHP Algorithm and multi-objective modeling has been done in a fuzzy manner considering a number of factors. The purposed model was solved by MOPSO Algorithm, and achieved answers from MOPSO were ranked by TOPSIS Algorithm. Finally, parameters sensitivity analysis of the model has been done.

One of the important issues in coordination of protective relays is minimization of the time interval between the operation of main and backup overcurrent (OC) relays. Overcurrent relays coordination problem due to the large number of variables and the nature of the objective functions can be introduced as a complex optimization problem that necessitates the need for an efficient optimization method with appropriate accuracy and speed. Given that for the realization of protective purposes including increased relays operation speed, selectivity, support, reliability and stability, different objective functions can be described, therefore, providing a multi-objective mathematical problem in optimizing protective problems may be necessary. In this regard, this article due to the capabilities of the MOPSO, proposes a multi-objective optimization structure, in which the SQP by increasing the speed and not increasing the search space is added to MOPSO. In this paper, several objective functions are proposed based on protection goals and optimal adjustment points have been extracted using the proposed multi-objective MOPSO-SQP. The simulations have been implemented and carefully analyzed on several typical power systems. The simulation results confirm the efficiency of the proposed Algorithm in ensuring the optimal coordination of protective overcurrent relays in power system.

The time-cost tradeoff problem is one of the most critical issues in the project scheduling field and so far, a lot of research has been done with a variety of quantitative and qualitative approaches on this subject. In this research, we intend to provide a two-objective mathematical model which balances crash and delay for activities. So that it provides the right tools for decision makers to decide on the scheduling of activities, with considering available facilities and time available, to complete the project. In the proposed mathematical model, it is attempting to use the assumptions such as the nonlinear cost function as well as the time value of money so that the problem conditions will be as close as possible to the real environment. At the end, we solve the mathematical model presented in this paper using the Multi Objective Particle Swarm Optimization (MOPSO) Algorithm and the impact of the crash and delay for activities in the final non-dominated solutions is investigated.

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.

In optimizing the portfolio, the main issue is the optimal selection of assets that can be bought with a certain amount of money. Although risk minimizing and revenue maximizing on investment seems simple, but in practice several approaches have been proposed for an optimal portfolio. In 1950, Harry Marquitz introduced his model in which proposed the optimization of the asset basket as a quadratic programing model with the aim of minimizing the variance of the asset set, provided that the expected return equals a constant value. In this research, the problem of three-objective optimization (i. e., maximizing stock returns, minimizing its risk and the third objective function, namely minimizing the number of assets) has been studied. Accordingly, investors, with admission a small amount of risk and a similar amount of return, will choose a basket of less assets. For this purpose, at first, genetic Algorithms and multi-Particle Swarm Optimization Algorithm were used to estimate the two-objective model of minimum variance and maximum return for better Algorithm identification. Then, with regard to the better performance of the Algorithm, this Algorithm was used to estimate the three-objective model for maximizing stock returns, minimizing risk, and minimizing the number of assets.