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 present study brings together the bene ts of the results of exergy, exergoeconomic, and exergoenvironmental analyses as well as Optimization of a wasteto-energy power plant. First, exergoeconomic balance for each stream was calculated. To validate the current simulations, the actual data from the Amsterdam waste-to-energy power plant in working conditions were examined. Moreover, the behaviors of the in uential parameters in the objective functions were evaluated. In order to perform multi-objective Optimization, Multi-Objective Particle-Swarm Optimization ((MOPSO)) algorithm was employed. To obtain the optimum operating conditions, 14 design parameters and 3 objective functions were taken into consideration, with the total cost rate, total exergy e ciency of the cycle, and environmental impacts as the objective functions. Finally, the TOPSIS decision-making method determined the optimum-operating conditions. The results of exergy analysis indicated that the most exergy destruction belonged to the incinerator unit at 66%. Instead, the pumps contributed the least in this  eld (approximately 1%). Under the in uence of the Optimization process, the total exergy e ciency of the power plant increased from 30. 89% to 38. 9% at the total cost of 5188. 05 USD/hour. A comparison of the obtained results from the Optimization procedure revealed that introducing optimum working condition would cause an increase in the exergy e ciency and a decrease in the exergy destruction among the components.

Financial Optimization is one of the most attractive areas in decision-making under uncertainty. Portfolio selection problem is a classical financial problem but it relies on three restrictive assumptions. In this paper, we propose the multi-stage mean-semivariance-Skewness portfolio Optimization problem under transaction cost. Solving the multi-stage portfolio Optimization problem is very challenging due to nonlinearity of the problem. Having modeled the problem, both Particle swarm Optimization and multi objective Particle swarm Optimization ((MOPSO)) algorithm are utilized to solve the presented model. Finally, some numerical examples are given to illustrate the effectiveness of the proposed approach and the feasibility of the (MOPSO) algorithm.

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).

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.

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.