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.

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.

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.

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.

Multi-label classification aims at assigning more than one label to each instance. Many real-world multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature selection is a common approach to tackle this issue by choosing prominent features. Multi-label feature selection is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence algorithm based on the binary algorithm of particle swarm optimization and a novel local search strategy has been proposed to select a set of prominent features. To this aim, features are divided into two categories based on the extension rate and the relationship between the output and the local search strategy to increase the convergence speed. The first group features have more similarity to class and less similarity to other features, and the second is redundant and less relevant features. Accordingly, a local operator is added to the particle swarm optimization algorithm to reduce redundant features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed algorithm compared to other algorithms presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of multi-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.

In this paper, recommended spiral passive micromixer was designed and simulated. spiral design has the potential to create and strengthen the centrifugal force and the secondary flow. A series of simulations were carried out to evaluate the effects of channel width, channel depth, the gap between loops, and flowrate on the micromixer performance. These features impact the contact area of the two fluids and ultimately lead to an increment in the quality of the mixture. In this study, for the flow rate of 25 μl/min and molecular diffusion coefficient of 1×10-10 m2/s, mixing efficiency of more than 90% is achieved after 30 (approximately one-third of the total channel length). Finally, the optimized design fabricated using proposed 3D printing method.

With the spread of applications of wireless sensor networks, in recent years, the use of this type of network in order to monitor the environment and analyze data collected from specific environments in a variety of ways has become very common. Wireless sensor networks are one of the best options for collecting data from the environment due to their easy configuration and no need for expensive equipment. The energy of sensors in wireless sensor networks is limited, which is a major challenge due to the lack of a fixed charge source. Because most of the sensors' energy is wasted during data transmission, a sensor that transmits more data than others and transmits data over long distances with packets will run out of energy sooner than others. When a sensor in the network runs out of energy, the network process may be disrupted. Therefore, due to the dynamic topology and distributed nature of wireless sensor networks, designing energy efficient routing protocols is one of the main challenges. Therefore, in this article, energy-aware routing protocol based on multi-objective particle swarm optimization algorithm is presented. In the proposed approach, the fitness function of the particle swarm optimization algorithm for selecting the optimal cluster head based on quality-of-service goals including residual energy, link quality, end-to-end delay and delivery rate. The simulation results show that the proposed approach has less energy consuming and extend network lifetime due to balancing the goals of quality-of-service criteria than other approaches.

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.