Real Power Loss Reduction by Enhanced Imperialist Competitive Algorithm

In this paper, an Enhanced Imperialist Competitive (EIC) Algorithm is proposed for solving reactive power problem. Imperialist Competitive Algorithm (ICA) which was recently introduced has shown its decent performance in optimization problems. This innovative optimization algorithm is inspired by socio-political progression of imperialistic competition in the real world. In the proposed EIC algorithm, the chaotic maps are used to adapt the angle of colonies movement towards imperialist’s position to augment the evading capability from a local optima trap. The ICA is candidly stuck into a local optimum when solving numerical optimization problems. To overcome this insufficiency, we use four different chaotic maps combined into ICA to augment the search ability. Proposed Enhanced Imperialist Competitive (EIC) algorithm has been tested on standard IEEE 30 bus test system and simulation results show clearly the decent performance of the proposed algorithm in reducing the real power loss

Simulated Tornado Optimization

We propose a swarm-based optimization algorithm inspired by air currents of a tornado. Two main air currents - spiral and updraft - are mimicked. Spiral motion is designed for exploration of new search areas and updraft movements is deployed for exploitation of a promising candidate solution. Assignment of just one search direction to each particle at each iteration, leads to low computational complexity of the proposed algorithm respect to the conventional algorithms. Regardless of the step size parameters, the only parameter of the proposed algorithm, called tornado diameter, can be efficiently adjusted by randomization. Numerical results over six different benchmark cost functions indicate comparable and, in some cases, better performance of the proposed algorithm respect to some other metaheuristics.Comment: 6 pages, 15 figures, 1 table, IEEE International Conference on Signal Processing and Intelligent System (ICSPIS16), Dec. 201

Multi-objective Optimization of Orbit Transfer Trajectory Using Imperialist Competitive Algorithm

This paper proposes a systematic direct approach to carry out effective multi-objective optimization of space orbit transfer with high-level thrust acceleration. The objective is to provide a transfer trajectory with acceptable accuracy in all orbital parameters while minimizing spacecraft fuel consumption. With direct control parameterization, in which the steering angles of thrust vector are interpolated through a finite number of nodes, the optimal control problem is converted into the parameter optimization problem to be solved by nonlinear programming. Besides the thrust vector direction angles, the thrust magnitude is also considered as variable and unknown along with initial conditions. Since the deviation of thrust vector in spacecraft is limited in reality, mathematical modeling of thrust vector direction is carried out in order to satisfy constraints in maximum deviation of thrust vector direction angles. In this modeling, the polynomial function of each steering angle is defined by interpolation of a curve based on finite number of points in a specific range with a nominal center point with uniform distribution. This kind of definition involves additional parameters to the optimization problem which results the capability of search method in satisfying constraint on the variation of thrust direction angles. Thrust profile is also modeled based on polynomial functions of time with respect to solid and liquid propellant rockets. Imperialist competitive algorithm is used in order to find optimal coefficients of polynomial for thrust vector and the optimal initial states within the transfer. Results are mainly affected by the degree of polynomials involved in mathematical modeling of steering angles and thrust profile which results different optimal initial states where the transfer begins. It is shown that the proposed method is fairly beneficial in the viewpoint of optimality and convergence. The optimality of the technique is shown by comparing the finite thrust optimization with the impulsive analysis. Comparison shows that the accuracy is acceptable with respect to fair precision in orbital elements and minimum fuel mass. Also, the convergence of the optimization algorithm is investigated by comparing the solution of the problem with other optimization techniques such as Genetic Algorithm. Results confirms the practicality of Imperialist Competitive Algorithm in finding optimum variation of thrust vector which results best transfer accuracy along with minimizing fuel consumption

Optimal Operation of Combined Photovoltaic Electrolyzer Systems

In this study, the design and simulation of a combination of a photovoltaic (PV) array with an alkaline electrolyzer is performed to maximize the production of hydrogen as a reliable power resource. Detailed electrical model of PV system, as long as thermal and electrochemical model of electrolyzer is used. Since an electrolyzer is a non-linear load, its coupling with PV systems to get the best power transfer is very important. Solar irradiation calculations were done for the region of Miami (FL, USA), giving an optimal surface slope of 25.7° for the PV array. The size of the PV array is optimized, considering maximum hydrogen production and minimum excess power production in a diurnal operation of a system using the imperialistic competitive algorithm (ICA). The results show that for a 10 kW alkaline electrolyzer, a PV array with a nominal power of 12.3 kW The results show that 12.3 kW photvoltaic system can be utilized for supplying a 10 kW electrolyzer. Hydrogen production and Faraday efficiency of the system are 697.21 mol and 0.3905 mol, respectively