1,452 research outputs found
A Hybrid Proposed Imperialist Competitive Algorithm with Conjugate Gradient Approach for Large Scale Global Optimization
This paper presents a novel hybrid imperialist competitive algorithm called ICA-CG algorithm. Such an algorithm combines the evolution ideas of the imperialist competitive algorithm and the classic optimization ideas of the conjugate gradient, based on the compensation for solving the large scale optimization. In the ICA-CG algorithm, the process of every iteration is divided into two stages. In the first stage, the randomly, rapidity and wholeness of the imperialist competitive Algorithm are used. In the second stage, one of the common optimization classical techniques, that called conjugate gradient to move imperialist countries, is used. Experimental results for five well known test problems have shown the superiority of the new ICA-CG algorithm, in large scale optimization, compared with the classical GA, ICA, PSO and ABC algorithms, with regard to the convergence of speed and quality of obtained solutions
Optimal allocation of FACTS devices in distribution networks using Imperialist Competitive Algorithm
Copyright © 2005-2015 Praise Worthy Prize. The publisher granted a permission to the author to archive this article in BURA.FACTS devices are used for controlling the voltage, stability, power flow and security of transmission lines. Imperialist Competitive is a recently developed optimization technique, used widely in power systems. This paper presents an approach to finding the optimal location and size of FACTS devices in a distribution network using the Imperialist Competitive technique. IEEE 30-bus system is used as a case study. The results show the advantages of the Imperialist Competitive technique over the conventional approaches. © 2013 Praise Worthy Prize S.r.l. - All rights reserved
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Optimal allocation of FACTS devices in power networks using imperialist competitive algorithm (ICA)
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDue to the high energy consumption demand and restrictions in the installation of new transmission lines, using Flexible AC Transmission System (FACTS) devices is inevitable. In power system analysis, transferring high-quality power is essential. In fact, one of the important factors that has a special role in terms of efficiency and operation is maximum power transfer capability. FACTS devices are used for controlling the voltage, stability, power flow and security of transmission lines. However, it is necessary to find the optimal location for these devices in power networks. Many optimization techniques have been deployed to find the optimal location for FACTS devices in power networks. There are several varieties of FACTS devices with different characteristics that are used for different purposes. The imperialist competitive algorithm (ICA) is a recently developed optimization technique that is used widely in power systems. This study presents an approach to find the optimal location and size of FACTS devices in power networks using the imperialist competitive algorithm technique. This technique is based on human social evolution. ICA technique is a new heuristic algorithm for global optimization searches that is based on the concept of imperialistic competition. This algorithm is used for mathematical issues; it can be categorized on the same level as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques. Also, in this study, the enhancement of voltage profile, stability and loss reduction and increasing of load-ability were investigated and carried out. In this case, to apply FACTS devices in power networks, the MATLAB program was used. Indeed, in this program all power network parameters were defined and analysed. IEEE 30-bus and IEEE 68-bus with 16 machine systems are used as a case study. All the simulation results, including voltage profile improvement and convergence characteristics, have been illustrated. The results show the advantages of the imperialist competitive algorithm technique over the conventional approaches
Optimal Design of Reinforced Concrete Cantilever Retaining Walls Utilizing Eleven Meta-Heuristic Algorithms: A Comparative Study
In this paper, optimum design of reinforced concrete cantilever retaining walls is performed under static and dynamic loading conditions utilizing eleven population-based meta-heuristic algorithms. These algorithms consist of Artificial Bee Colony algorithm, Big Bang-Big Crunch algorithm, Teaching-Learning-Based Optimization algorithm, Imperialist Competitive Algorithm, Cuckoo Search algorithm, Charged System Search algorithm, Ray Optimization algorithm, Tug of War Optimization algorithm, Water Evaporation Optimization algorithm, Vibrating Particles System algorithm, and Cyclical Parthenogenesis Algorithm. Two well-known methods consisting of the Rankine and Coulomb methods are used to determine lateral earth pressures acting on cantilever retaining wall under static loading condition. In addition, Mononobe-Okabe method is employed for dynamic loading condition. The design is based on ACI 318-05 and the goal of optimization is to minimize the cost function of the cantilever retaining wall. The performance of the utilized algorithms is investigated through an optimization example of cantilever retaining wall. In addition, convergence histories of the algorithms are provided for better understanding of their performance
Resource allocation in cloud computing using advanced imperialist competitive algorithm
Cloud computing makes possible free access to computing resources and high-level services for performing complex calculations and mass storage of information on the Internet. Resource management is one of the most important tasks of cloud providers, which is known as resource allocation. Heterogeneous resources and diverse requests at different time intervals makes it difficult to solve resources allocation problems and is considered as a NP-hard problem. Providing an efficient algorithm for resources allocation to satisfy the cloud providers and customers has always attracted much attention of researchers. Heuristic methods have always introduced as a good model for problem solving. However, most algorithms suffer from early convergence. This paper proposes a new approach based on imperialist competitive algorithm (ICA) which emphasizes the optimization of resource allocation in reducing time, cost and energy consumption. The proposed approach has been able to improve the early convergence of colonial competition algorithm by combining with the Tabu Search Algorithm to achieve an optimal solution at an acceptable time. The evaluated results show more efficiency performance than several relevant effective algorithms
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A Metaheuristic Adaptive Cubature Based Algorithm to Find Bayesian Optimal Designs for Nonlinear Models
Finding Bayesian optimal designs for nonlinear models is a difficult task because the optimality criteriontypically requires us to evaluate complex integrals before we perform a constrained optimization. Wepropose a hybridized method where we combine an adaptive multidimensional integration algorithm anda metaheuristic algorithm called imperialist competitive algorithm to find Bayesian optimal designs. Weapply our numerical method to a few challenging design problems to demonstrate its efficiency. Theyinclude finding D-optimal designs for an item response model commonly used in education, Bayesianoptimal designs for survivalmodels, and Bayesian optimal designs for a four-parameter sigmoid Emax doseresponse model. Supplementary materials for this article are available online and they contain an R packagefor implementing the proposed algorithm and codes for reproducing all the results in this paper
Parameter estimation of electric power transformers using Coyote Optimization Algorithm with experimental verification
In this work, the Coyote Optimization Algorithm (COA) is implemented for estimating the parameters of single and three-phase power transformers. The estimation process is employed on the basis of the manufacturer's operation reports. The COA is assessed with the aid of the deviation between the actual and the estimated parameters as the main objective function. Further, the COA is compared with well-known optimization algorithms i.e. particle swarm and Jaya optimization algorithms. Moreover, experimental verifications are carried out on 4 kVA, 380/380 V, three-phase transformer and 1 kVA, 230/230 V, single-phase transformer. The obtained results prove the effectiveness and capability of the proposed COA. According to the obtained results, COA has the ability and stability to identify the accurate optimal parameters in case of both single phase and three phase transformers; thus accurate performance of the transformers is achieved. The estimated parameters using COA lead to the highest closeness to the experimental measured parameters that realizes the best agreements between the estimated parameters and the actual parameters compared with other optimization algorithms
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
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