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In this work a strong framework is presented for solving the constrained nonlinear optimization problem that is a relatively complicated problem. These problems arise in a diverse range of sciences. There are a number of different approaches have been proposed. In this work, we employ the imperialist compet-itive algorithm (ICA) for solving constrained nonlinear optimization problems. Some well-known problems are presented to demonstrate the efficiency of this new robust optimization method in comparison to other known methods.
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... Huan-Tong et al. who proposed a modified evolution strategy based on probability ranking method to solve complicated nonlinear systems of equations [5]. Abdollahi et al. applied imperialist competitive algorithm (ICA) for solving nonlinear systems equations [6]. Ouyang et al. employed a hybrid particle swarm optimization algorithm. ...
... The obtained answers of mathematical methods are sensitive to the initial guess of the solution [9,11]. The population size of the evolutionary algorithms (GA [1], EGA [3], EA [10], PEA [2], MES [5], MSEOA [8], PSO [7], PPSO [12], ASA [13], ICA [6] and IWO [15]) is large and the convergence of the mentioned methods to the global minimum is slow. For this reason, it is necessary to find an efficient algorithm for solving the systems of nonlinear equations. ...
... The comparison of convergence history of ICA, COA and iCOA methods are presented in Figs. 3,4,5,6,7,8,9,10. The quality of the obtained solutions by iCOA in all of the tests and case studies is better. ...
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Systems of nonlinear equations come into different range of sciences such as chemistry, economics, medicine, robotics, engineering, and mechanics. There are different methods for solving systems of nonlinear equations such as Newton type methods, imperialist competitive algorithm, particle swarm algorithm, conjugate direction method that each has their own advantages and weaknesses such as low convergence speed and poor quality of solutions. This paper improves cuckoo optimization algorithm for solving systems of nonlinear equations by changing the policy of egg laying radius, and some well-known problems are presented to demonstrate the efficiency and better performance of this new robust optimization algorithm. From obtained results, our approach found more accurate solutions with the lowest number of function evaluations.
... In recent years, with the developments of the stochastic algorithms, more and more scholars have realized the superiority of the stochastic algorithms and tried to solve the systems of nonlinear equations with them. Abdollahi et al. [3] employed imperialist competitive algorithm (ICA) to solve nonlinear equations systems and demonstrated the efficiency of this method with some well-known test problems; Raja et al. [4] developed a novel and efficient intelligent computing approach for solving nonlinear equations using evolutionary computational technique based on twelve variants of genetic algorithm (GA); Subsequently, in 2016, Raja et al. [5] introduced another memetic approach GA-SQP, which used variants of GA as a tool for global search method and sequential quadratic programming (SQP) for efficient local search and obtained satisfactory results; Meanwhile, Raja et al. [6] proposed a hybrid particle swarm optimization algorithm PSO-NMM by combining the strength of particle swarm optimization with Nelder-Mead method and solved some nonlinear benchmark models successfully. Grosan and Abraham [7] applied an evolutionary computation technique to solve the problem obtained by transforming the system into a multiobjective optimization problem; Mo et al. [8] introduced conjugate direction method into particle swarm optimization (PSO) in order to improve PSO and enable PSO to effectively optimize high-dimensional optimization problem and proposed the conjugate direction particle swarm optimization (CDPSO); Jaberipour et al. [9] used an improved particle swarm algorithm(PPSO) to solve the systems of nonlinear equations; Oliveira and Petraglia [10] proposed the fuzzy adaptive simulated annealing (ASA) for finding solutions of arbitrarily nonlinear systems of functional equations; Pourjafari et al. [11] introduced a novel optimization method based on invasive weed optimization (IWO), respectively, for finding all roots of systems of nonlinear equations; Pourrajabian et al. [12] and Muhammad et al. [13] used the genetic algorithm to study the solving systems of nonlinear equations, respectively. ...
... x 3 1.00000000e?00 3.86215466e-06 -9.66722245e-09 -6.77638053e-09 6.75869742e-01 -8.4068221e-01 -9.97038023e-01 1.95314704e-01 9.4576852e-01 6.9280787e-01 7.7919009e-01-7.1268794e-01 ...
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In the present paper, a modified cuckoo search algorithm is proposed for solving nonlinear equations, that is, the niche cuckoo search algorithm (NCSA) based on fitness-sharing principle. Niche strategy is introduced to enhance the ability of the cuckoo search algorithm to solve nonlinear equations. So as to evaluate the efficiency of NCSA, NCSA has been first benchmarked by twenty standard test functions by comparing with standard genetic algorithm, chaos gray-coded genetic algorithm and standard cuckoo search algorithm. Then, solutions for several examples of nonlinear systems are presented and compared with results obtained by other approaches. Moreover, the sensitivity analysis of the method to initial interval, nests number, probability and niche number has been studied. Comparison results reveal that the proposed algorithm can cope with the highly nonlinear problems and outperforms many algorithms which exist in the literature.
... Thus, according to the existing infrastructures in Iran and the budget for the implementation of next generation networks, the permutation method seems more appropriate. As a future work, we are planning to optimize the quality of service (QOS) in NGN with evolutionary optimization algorithms such as genetic algorithm, particle swarm algorithm, imperialist competitive algorithm, and cuckoo optimization algorithm [16][17][18][19][20]. ...
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In recent years, telecom operators have taken big steps towards Next Generation Network (NGN). Although the concept of NGN and the need to pass it, is known, but discussion on different strategies for migration still continues. The migration to NGN should be done with the best design, the lowest cost, the fastest time and the least error for operators who intend to migrate. Due to the complex nature of this topic, the main goal of this paper is to identify the common steps that telecom service providers consider to migrate to NGN, and previously published papers are intended as a basis for research. This paper covers the analysis of the capabilities of NGN, preparation steps, and migration scenarios to NGN.
... The simulation of human gradual evolution caused the creation of random optimization methods which were called evolutionary algorithms. We can name some different types of evolutionary algorithms such as genetic algorithm, particle swarm algorithm, imperialist competitive algorithm, and cuckoo optimization algorithm [4][5][6][7][8]. In the following, we discuss the structure of genetic algorithm, particle swarm algorithm, and hybrid algorithm. ...
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Nowadays unstructured mobile networks (Mobile Ad Hoc Networks) are of utmost importance that various aspects of these networks are the subject of many investigations. In this type of networks, information is transferred in the form of data packets using wireless technology. To transfer information from the source node to the destination node, the optimal path must be chosen. In this study, a hybrid algorithm for routing in ad hoc networks is introduced. Evaluations conducted show that the proposed hybrid algorithm finds the optimal path with a relative decrease in all criteria compared to the previous methods. Index Terms— MANET (Mobile Ad-hoc Network), Quality of Service (QOS), Evolutionary Algorithm (EA), Routing.
... Later on there came RST dependent algorithms like Genetic Algorithms (GA) and their hybrids [5], [10], [13], [14] and [15] which are based on evolution and which were universally used by many researchers to solve many complex non-linear problems. There are also many more evolutionary and heuristic algorithms and their hybrids and extensions which have been used in the study of these complex non-linear problems like the tabu search algorithms [15], SQP [6], Particle Swarm Optimization (PSO) [1], [3], [5], [8], Ant Colony Optimization (ACO) [9], Imperialist Competitive Algorithms (ICA) [2], Lineup competition algorithms [11], Big bang-big crunch [7] and many more. There has also been a lot of work going on for non-linear problems that are unconstrained [4]. ...
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