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.

Today, it is important to reduce the original huge data set to a manageable volume. Also, unbalanced data distribution between different classes is a serious challenge in data mining. In the proposed method, the instance reduction problem is considered as a MULTI-OBJECTIVE problem, which can perform well by considering the two contradict criteria, classification accuracy and reduction rate of instances. The MULTI-OBJECTIVE problem is solved using the chaotic PARTICLE SWARM OPTIMIZATION algorithm. The distance-based decision classifier has the task of distinguishing the maintenance or deletion of test instances. Creating and maintaining balances for different types of data distribution is the main goal of the proposed method. The results of the experiments have been compared with the state-of-the-art methods, which show superiority of the proposed method in terms of classification accuracy and reduction percentage.

In this paper, MULTI-OBJECTIVE OPTIMIZATION of sandwich panels with open and prismatic core has been studied. Naming these panels is based on the number of corrugations (n) of the core. The panel is considered as a heat exchanger that is loaded under longitudinal loading simultaneously. MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION (MOPSO) is used by considering weight and heat transfer index as OBJECTIVE function. OPTIMIZATION is carried out so that the panel has minimum weight and maximum heat transfer index simultaneously; moreover it will not suffer from yielding and buckling in face and core plates. The results showed that two panels, i.e. n=1 and n=7 are very suitable in one-OBJECTIVE and two-OBJECTIVE OPTIMIZATIONs. Also, maximum of heat transfer index obtained by a certain panel is nearly the same in various loadings. Pareto diagrams achieved out of two-OBJECTIVE OPTIMIZATION have two separate areas where in one area weight increase may cause an intense increase in heat transfer index and in another area this index remains almost constant. The diagrams are helpful in selecting suitable panel and its geometric dimensions based on significance of each OBJECTIVE functions. Comparing the results indicate efficiency of PSO method in one-OBJECTIVE and two-OBJECTIVE OPTIMIZATION of the panels.

A MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION (MOPSO) algorithm has been used to design a classifier which is able to optimize some important pattern recognition indices concurrently. These are Reliability, Score of recognition, and the number of hyperplanes. The proposed classifier can efficiently approximate the decision hyperplanes for separating the different classes in the feature space and dose not has any over-fitting and over-learning problems.Other SWARM intelligence based classifiers do not have the capability of simultaneous optimizing aforesaid indices and they also may suffer the over-fitting problem.The experimental results show that the proposed MULTI-OBJECTIVE classifier can estimate the optimum sets of hyperplanes by approximating the Pareto-front and provide the favorite user's setup for selecting aforesaid indices.

PARTICLE SWARM OPTIMIZATION (PSO) is a widely recognized bio-inspired algorithm for systematically exploring solution spaces and iteratively iden-tifying optimal points. Through updating local and global best solutions, PSO effectively explores the search process, enabling the discovery of the most advantageous outcomes. This study proposes a novel Smith chart-based PARTICLE SWARM OPTIMIZATION to solve convex and nonconvex MULTI-OBJECTIVE engineering problems by representing complex plane values in a polar coordinate system. The main contribution of this paper lies in the utilization of the Smith chart’s impedance and admittance circles to dynamically update the location of each PARTICLE, thereby effectively deter-mining the local best PARTICLE. The proposed method is applied to three test functions with different behaviors, namely concave, convex, noncon-tinuous, and nonconvex, and performance parameters are examined. The simulation results show that the proposed strategy offers successful conver-gence performance for MULTI-OBJECTIVE OPTIMIZATION applications and meets performance expectations with a well-distributed solution set.

With the increased diversity of the customer demand and complexity of the product, Inconel 825 is widely used to meet the actual needs, especially in the aerospace industry. It is difficult-to-cut material because of its high toughness and hardness. The present research attempts to optimize the process parameters of wire electric discharge machining during the cutting operation of Inconel 825. The wire electric discharge machining characteristics such as pulse-ontime, pulse-off time, spark gap voltage, peak current, wire tension, wire feed aretaken into consideration. The performance was measured in terms of materialremoval rate, surface roughness, and wire wear ratio. The central compositedesign of response surface methodology at an α value of ± 2 was employed toestablish the mathematical model between process parameters and performancemeasures. A MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION algorithm has beenused to find the optimal solutions called Pareto optimal solutions. It uses theconcept of dominance to find the non dominated set in the entire population andthe crowding distance approach to finding the best Pareto optimal solutions witha good diversity of OBJECTIVEs. The confirmation experiments of the MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION algorithm show a significantimprovement in material removal rate (27. 934 to 31. 687 mm2/min), surfaceroughness (2. 689 to 2. 448μ m), and wire wear ratio (0. 027 to 0. 030). SEMmicrograph studies showed the number of cracks, pockmarks, craters, andpulled out material on the workpiece and wire electrode surface. EnergyDispersive X-ray analysis is performed to investigate the presence of elementson the work surface other than the base material.

Placement process is one of the vital stages in physical design. In this stage, modules and elements of the circuit are placed in distinct locations based on OPTIMIZATION processes. Hence, each placement process influences one or more OPTIMIZATION factor. On the other hand, it can be stated unequivocally that FPGA is one of the most important and applicable devices in our electronic world. So, it is vital to spend time for better learning of its structure. VLSI science looks for new techniques for minimizing the expense of FPGA in order to gain better performance. Diverse algorithms are used for running FPGA placement procedures. It is known that PARTICLE SWARM OPTIMIZATION (PSO) is one of the practical evolutionary algorithms for this kind of applications. So, this algorithm is used for solving placement problems. In this work, a novel method for optimized FPGA placement has been used. According to this process, the goal is to optimize two OBJECTIVEs defined as wire length and overlap removal functions. Consequently, we are forced to use MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION (MOPSO) in the algorithm. Structure of MOPSO is such that it introduces set of answers among which we have tried to find a unique answer with minimum overlap. It is worth noting that discrete nature of FPGA blocks forced us to use a discrete version of PSO. In fact, we need a combination of MULTI-OBJECTIVE PSO and discrete PSO for achieving our goals in OPTIMIZATION process. Tested results on some of FPGA benchmark (MCNC benchmark) are shown in “experimental results” section, compared with popular method “VPR”. These results show that proper selection of FPGA’s size and reasonable number of blocks can give us good response.

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.

Increasing population and food demand, disproportionate cultivation and annual production of various agricultural products with market needs and low productivity of the agricultural sector and the loss of water and soil resources have made it necessary to determine and implement the country's optimal cropping pattern. In this study, due to the limitations and problems of classical methods in order to reduce processing time and improve the quality of solutions, the MULTI-OBJECTIVE Chaotic PARTICLE SWARM OPTIMIZATION was used to determine the optimal cultivation pattern of Sistan plain in optimal conditions and deficit irrigation. The results of the MULTI-OBJECTIVE Chaotic PARTICLE SWARM OPTIMIZATION for the dominant cultures in the region showed that the current cropping pattern of the region is not optimal and with the implementation of the proposed model, the profit per unit area under cultivation will increase. The results of application of deficit irrigation during different growing periods of wheat, barley, alfalfa, sorghum, watermelon and grapes showed that applying deficit irrigation in this plain is not a good strategy and therefore only a full irrigation strategy is recommended. The results of sensitivity analysis of the model showed that at low prices, farmers reaction is less and at higher prices more reaction to price changes and with increasing prices, the program efficiency is lower.