F.2.7. Optimization
Mahsa Dehbozorgi; Pirooz Shamsinejadbabaki; Elmira Ashoormahani
Abstract
Clustering is one of the most effective techniques for reducing energy consumption in wireless sensor networks. But selecting optimum cluster heads (CH) as relay nodes has remained as a very challenging task in clustering. All current state of the art methods in this era only focus on the individual ...
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Clustering is one of the most effective techniques for reducing energy consumption in wireless sensor networks. But selecting optimum cluster heads (CH) as relay nodes has remained as a very challenging task in clustering. All current state of the art methods in this era only focus on the individual characteristics of nodes like energy level and distance to the Base Station (BS). But when a CH dies it is necessary to find another CH for cluster and usually its neighbor will be selected. Despite existing methods, in this paper we proposed a method that considers node neighborhood fitness as a selection factor in addition to other typical factors. A Particle Swarm Optimization algorithm has been designed to find best CHs based on intra-cluster distance, distance of CHs to the BS, residual energy and neighborhood fitness. The proposed method compared with LEACH and PSO-ECHS algorithms and experimental results have shown that our proposed method succeeded to postpone death of first node by 5.79%, death of 30% of nodes by 25.50% and death of 70% of nodes by 58.67% compared to PSO-ECHS algorithm
F.2.7. Optimization
F. Fouladi Mahani; A. Mahanipour; A. Mokhtari
Abstract
Recently, significant interest has been attracted by the potential use of aluminum nanostructures as plasmonic color filters to be great alternatives to the commercial color filters based on dye films or pigments. These color filters offer potential applications in LCDs, LEDs, color printing, CMOS image ...
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Recently, significant interest has been attracted by the potential use of aluminum nanostructures as plasmonic color filters to be great alternatives to the commercial color filters based on dye films or pigments. These color filters offer potential applications in LCDs, LEDs, color printing, CMOS image sensors, and multispectral imaging. However, engineering the optical characteristics of these nanostructures to design a color filter with desired pass-band spectrum and high color purity requires accurate optimization techniques. In this paper, an optimization procedure integrating genetic algorithm with FDTD Solutions has been utilized to design plasmonic color filters, automatically. Our proposed aluminum nanohole arrays have been realized successfully to achieve additive (red, green, and blue) color filters using the automated optimization procedure. Despite all the considerations for fabrication simplicity, the designed filters feature transmission efficacies of 45-50 percent with a FWHM of 40 nm, 50 nm, and 80 nm for the red, green, and blue filters, respectively. The obtained results prove an efficient integration of genetic algorithm and FDTD Solutions revealing the potential application of the proposed method for automated design of similar nanostructures.
F.2.7. Optimization
M. YousefiKhoshbakht; N. Mahmoodi Darani
Abstract
Abstract: The Open Vehicle Routing Problem (OVRP) is one of the most important extensions of the vehicle routing problem (VRP) that has many applications in industrial and service. In the VRP, a set of customers with a specified demand of goods are given and a depot where a fleet of identical capacitated ...
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Abstract: The Open Vehicle Routing Problem (OVRP) is one of the most important extensions of the vehicle routing problem (VRP) that has many applications in industrial and service. In the VRP, a set of customers with a specified demand of goods are given and a depot where a fleet of identical capacitated vehicles is located. We are also given the ‘‘traveling costs’’ between the depot and all the customers, and between each pair of customers. In the OVRP against to VRP, vehicles are not required to return to the depot after completing service. Because VRP and OVRP belong to NP-hard Problems, an efficient hybrid elite ant system called EACO is proposed for solving them in the paper. In this algorithm, a modified tabu search (TS), a new state transition rule and a modified pheromone updating rule are used for more improving solutions. These modifications lead that the proposed algorithm does not trapped at the local optimum and discovers different parts of the solution space. Computational results on fourteen standard benchmark instances for VRP and OVRP show that EACO finds the best known solutions for most of the instances and is comparable in terms of solutions quality to the best performing published metaheuristics in the literature.
F.2.7. Optimization
M. Kosari; M. Teshnehlab
Abstract
Although many mathematicians have searched on the fractional calculus since many years ago, but its application in engineering, especially in modeling and control, does not have many antecedents. Since there are much freedom in choosing the order of differentiator and integrator in fractional calculus, ...
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Although many mathematicians have searched on the fractional calculus since many years ago, but its application in engineering, especially in modeling and control, does not have many antecedents. Since there are much freedom in choosing the order of differentiator and integrator in fractional calculus, it is possible to model the physical systems accurately. This paper deals with time-domain identification fractional-order chaotic systems where conventional derivation is replaced by a fractional one with the help of a non-integer derivation. This operator is itself approximated by a N-dimensional system composed of an integrator and a phase-lead filter. A hybrid particle swarm optimization (PSO) and genetic algorithm (GA) method has been applied to estimate the parameters of approximated nonlinear fractional-order chaotic system that modeled by a state-space representation. The feasibility of this approach is demonstrated through identifying the parameters of approximated fractional-order Lorenz chaotic system. The performance of the proposed algorithm is compared with the genetic algorithm (GA) and standard particle swarm optimization (SPSO) in terms of parameter accuracy and cost function. To evaluate the identification accuracy, the time-domain output error is designed as the fitness function for parameter optimization. Simulation results show that the proposed method is more successful than other algorithms for parameter identification of fractional order chaotic systems.
F.2.7. Optimization
R. Roustaei; F. Yousefi Fakhr
Abstract
The human has always been to find the best in all things. This Perfectionism has led to the creation of optimization methods. The goal of optimization is to determine the variables and find the best acceptable answer Due to the limitations of the problem, So that the objective function is minimum or ...
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The human has always been to find the best in all things. This Perfectionism has led to the creation of optimization methods. The goal of optimization is to determine the variables and find the best acceptable answer Due to the limitations of the problem, So that the objective function is minimum or maximum. One of the ways inaccurate optimization is meta-heuristics so that Inspired by nature, usually are looking for the optimal solution. in recent years, much effort has been done to improve or create metaheuristic algorithms. One of the ways to make improvements in meta-heuristic methods is using of combination. In this paper, a hybrid optimization algorithm based on imperialist competitive algorithm is presented. The used ideas are: assimilation operation with a variable parameter and the war function that is based on mathematical model of war in the real world. These changes led to increase the speed find the global optimum and reduce the search steps is in contrast with other metaheuristic. So that the evaluations done more than 80% of the test cases, in comparison to Imperialist Competitive Algorithm, Social Based Algorithm , Cuckoo Optimization Algorithm and Genetic Algorithm, the proposed algorithm was superior.
F.2.7. Optimization
M. Mohammadpour; H. Parvin; M. Sina
Abstract
Many of the problems considered in optimization and learning assume that solutions exist in a dynamic. Hence, algorithms are required that dynamically adapt with the problem’s conditions and search new conditions. Mostly, utilization of information from the past allows to quickly adapting changes ...
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Many of the problems considered in optimization and learning assume that solutions exist in a dynamic. Hence, algorithms are required that dynamically adapt with the problem’s conditions and search new conditions. Mostly, utilization of information from the past allows to quickly adapting changes after. This is the idea underlining the use of memory in this field, what involves key design issues concerning the memory content, the process of update, and the process of retrieval. In this article, we used chaotic genetic algorithm (GA) with memory for solving dynamic optimization problems. A chaotic system has much more accurate prediction of the future rather than random system. The proposed method used a new memory with diversity maximization. Here we proposed a new strategy for updating memory and retrieval memory. Experimental study is conducted based on the Moving Peaks Benchmark to test the performance of the proposed method in comparison with several state-of-the-art algorithms from the literature. Experimental results show superiority and more effectiveness of the proposed algorithm in dynamic environments.
F.2.7. Optimization
E. Khodayari; V. Sattari-Naeini; M. Mirhosseini
Abstract
Developing optimal flocking control procedure is an essential problem in mobile sensor networks (MSNs). Furthermore, finding the parameters such that the sensors can reach to the target in an appropriate time is an important issue. This paper offers an optimization approach based on metaheuristic methods ...
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Developing optimal flocking control procedure is an essential problem in mobile sensor networks (MSNs). Furthermore, finding the parameters such that the sensors can reach to the target in an appropriate time is an important issue. This paper offers an optimization approach based on metaheuristic methods for flocking control in MSNs to follow a target. We develop a non-differentiable optimization technique based on the gravitational search algorithm (GSA). Finding flocking parameters using swarm behaviors is the main contributing of this paper to minimize the cost function. The cost function displays the average of Euclidean distance of the center of mass (COM) away from the moving target. One of the benefits of using GSA is its application in multiple targets tracking with satisfying results. Simulation results indicate that this scheme outperforms existing ones and demonstrate the ability of this approach in comparison with the previous methods.
F.2.7. Optimization
B. Safaee; S. K. Kamaleddin Mousavi Mashhadi
Abstract
Quad rotor is a renowned underactuated Unmanned Aerial Vehicle (UAV) with widespread military and civilian applications. Despite its simple structure, the vehicle suffers from inherent instability. Therefore, control designers always face formidable challenge in stabilization and control goal. In this ...
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Quad rotor is a renowned underactuated Unmanned Aerial Vehicle (UAV) with widespread military and civilian applications. Despite its simple structure, the vehicle suffers from inherent instability. Therefore, control designers always face formidable challenge in stabilization and control goal. In this paper fuzzy membership functions of the quad rotor’s fuzzy controllers are optimized using nature-inspired algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Finally, the results of the proposed methods are compared and a trajectory is defined to verify the effectiveness of the designed fuzzy controllers based on the algorithm with better results.
F.2.7. Optimization
M.M Abravesh; A Sheikholeslami; H. Abravesh; M. Yazdani asrami
Abstract
Metal oxide surge arrester accurate modeling and its parameter identification are very important for insulation coordination studies, arrester allocation and system reliability. Since quality and reliability of lightning performance studies can be improved with the more efficient representation of the ...
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Metal oxide surge arrester accurate modeling and its parameter identification are very important for insulation coordination studies, arrester allocation and system reliability. Since quality and reliability of lightning performance studies can be improved with the more efficient representation of the arresters´ dynamic behavior. In this paper, Big Bang – Big Crunch and Hybrid Big Bang – Big Crunch optimization algorithms are used to selects optimum surge arrester model equivalent circuit parameters values, minimizing the error between the simulated peak residual voltage value and this given by the manufacturer.The proposed algorithms are applied to a 63 kV and 230 kV metal oxide surge arrester. The obtained results show that using this method the maximum percentage error is below 1.5 percent.
F.2.7. Optimization
M. Maadi; M. Javidnia; M. Ghasemi
Abstract
Nowadays, due to inherent complexity of real optimization problems, it has always been a challenging issue to develop a solution algorithm to these problems. Single row facility layout problem (SRFLP) is a NP-hard problem of arranging a number of rectangular facilities with varying length on one side ...
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Nowadays, due to inherent complexity of real optimization problems, it has always been a challenging issue to develop a solution algorithm to these problems. Single row facility layout problem (SRFLP) is a NP-hard problem of arranging a number of rectangular facilities with varying length on one side of a straight line with aim of minimizing the weighted sum of the distance between all facility pairs. In this paper two new algorithms of cuckoo optimization and forest optimization are applied and compared to solve SRFLP for the first time. The operators of two algorithms are adapted according to the characteristics of SRFLP and results are compared for two groups of benchmark instances of the literature. These groups consist of instances with the number of facilities less and more than 30. Results on two groups of instances show that proposed cuckoo optimization based algorithm has better performance rather than proposed forest optimization based algorithm in both aspects of finding the best solution and Computational time.
F.2.7. Optimization
F. Tatari; M. B. Naghibi-Sistani
Abstract
In this paper, the optimal adaptive leader-follower consensus of linear continuous time multi-agent systems is considered. The error dynamics of each player depends on its neighbors’ information. Detailed analysis of online optimal leader-follower consensus under known and unknown dynamics is presented. ...
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In this paper, the optimal adaptive leader-follower consensus of linear continuous time multi-agent systems is considered. The error dynamics of each player depends on its neighbors’ information. Detailed analysis of online optimal leader-follower consensus under known and unknown dynamics is presented. The introduced reinforcement learning-based algorithms learn online the approximate solution to algebraic Riccati equations. An optimal adaptive control technique is employed to iteratively solve the algebraic Riccati equation based on the online measured error state and input information for each agent without requiring the priori knowledge of the system matrices. The decoupling of the multi-agent system global error dynamics facilitates the employment of policy iteration and optimal adaptive control techniques to solve the leader-follower consensus problem under known and unknown dynamics. Simulation results verify the effectiveness of the proposed methods.
