Forecasting tax revenues is vitally important issue for optimal allocation of taxable resources, planning and budgeting in national and regional levels and knowing the potential national participation in public expenditures. The classical Optimization based on mathematical methods may not be reliable in real world and mostly inefficient and inapplicable in complicated world due to their restricted assumptions. The smart Optimization may help us to find the solution.This essay based on modified PSO methodology. The initial trial based on the data during 1971- 2007 in case of various direct and indirect taxes, and using updated data during 2008- 2012 for final forecasting, to estimate tax revenues for upcoming next three years (2013 up to 2016) by MATLAB software.

In this research, multi-period stock portfolio selection was modeled and solved under uncertainty and considering transaction costs. A possibilistic mean-semivariance-entropy model for multi-period portfolio selection by taking into account four criteria viz., return, risk, diversification degree of portfolio and transaction cost was introduced. In this model, the return level by the possibilistic mean value of return, the risk level by the lower possibilistic semivariance of return, and the diversification degree of portfolio was quantified by the possibilistic entropy. We used fuzzy theory in order to consider uncertainty in proposed model and considered asset returns as trapezoidal fuzzy numbers. MOPSO algorithm was used to solve the model. In order to evaluate the proposed models performance, a similar model including proportional entropy was modeled and solved and its results were compared with the possibilistic entropy model. The results of this comparison showed that the possibilistic entropy model is better than the proportional entropy model because it provides better efficiency frontier. Regarding the optimized portfolios in one-time implementation of the algorithm on the possibilistic entropy model in third-time period, the highest percentage of stocks selected in the optimal portfolio of risk seeker, risk averse and risk neutral investor is respectively kagol,hakeshti and shekhark.

Science progress has introduced new issues into the world requiring Optimization algorithm with fast adaptation with uncertain environment changing with time. In these issues, location and optimized value change over time, so Optimization algorithm should be capable of fast adaptation with variable conditions. This study has proposed a new algorithm based on particle Optimization algorithm called Adaptive Increasing/Decreasing PSO. This algorithm, adaptively with an increase and decrease in the number of algorithm particles and effective search limit, is capable of searching and finding optimized number changed with time in non-linear and dynamic environments with undetectable changes. Also, a new definition, focused search zone, is provided for signalizing hopeful areas in order to accelerate local search process and prevent premature convergence, and success index as an indicator of the behavior of centralized search area in relation to environmental conditions. Results of the proposed algorithm on the moving peaks benchmark were assessed and compared with the results of some other studies. Results show positive effects of adaptive mechanisms such as a decrease and an increase in the particles and search limit on the duration of searching and finding Optimization in comparison with other multi-population based Optimization algorithms.

In this paper, optimal design of latticed columns is performed under static loads utilizing two new algorithms, Colliding Bodies Optimization (CBO) and Democratic Particles Swarm Optimization (DPSO) and also a comparison between two valid codes, AISC 360-10 and Eurocode 3, is investigated. This Optimization is on the basis of cost function of materials used in latticed columns, according to each standard and their constructions. Three examples are optimized for each code and their convergence curves are compared. Finally a comparison between two codes is done and the most optimum standard is presented.

In this paper, a new hybrid Particle Swarm Optimization (PSO) and Harmony Search (HS) algorithm, denoted by PSOHS is presented. This hybrid algorithm is designed to improve the efficiency of the PSO and remove some of the disadvantages which reduce the capability of the PSO. The main problem of the PSO is the lack of balance between exploration and exploitation of the algorithm. Another problem is how to handle the violating particles from feasible search space without reduction in the performance of the algorithm. The problem of unbalanced exploration and exploitation is solved using linear varying inertia weight. The second problem is solved in some other algorithms via reproduction of the violating particles using the HS algorithm. In this paper, these two approaches are combined to achieve a more efficient algorithm for engineering design problems. To show the higher capability of this approach compared to other works, several benchmark engineering examples, which have been considered previously and solved utilizing a variety of Optimization algorithms, is solved by the present hybrid algorithm. Results illustrate a desirable performance of the PSOHS in both obtaining lower weight and having a higher convergence rate.

Sensor networks consisting of hundreds of thousands of nodes are usually referred to as low-power and lossy networks (LLNs). In these networks, nodes are interconnected by low data-rate lossy links, which have low stability due to low packet delivery rates. Since routing protocols play an important role in the performance of sensor networks, the required protocols must be designed to address energy-related issues. This helps not only to reduce the energy consumption of the overall network but also to evenly distribute the energy load among the network nodes to increase the lifetime of the network. In such networks, the routing protocol for low-power and lossy network (RPL) can minimize the existing issues and improve performance. This paper aims at minimizing energy consumption by specifying the best path in the network for data transmission. To achieve this aim, the particle swarm Optimization (PSO) algorithm is used. This algorithm shows a good convergence for the movement of particles towards the best path and presents the best solution with the maximum number of particles in different particle velocity ranges. The results of this study show that as the number of particles increases, the cost function decreases by 58% on average, and thus, lower energy is required to reach the root. Moreover, with increasing velocity range, the cost function decreases by 43% on average. The proposed algorithm shows a 43% decrease in energy consumption on average.