• International Journal of Control, Automation, and Systems
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• v.5 no.4
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• pp.444-455
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• 2007

An approach to construct multiple interpretable and precise fuzzy systems based on the Pareto Multi-objective Cooperative Coevolutionary Algorithm (PMOCCA) is proposed in this paper. First, a modified fuzzy clustering algorithm is used to construct antecedents of fuzzy system, and consequents are identified separately to reduce computational burden. Then, the PMOCCA and the interpretability-driven simplification techniques are executed to optimize the initial fuzzy system with three objectives: the precision performance, the number of fuzzy rules and the number of fuzzy sets; thus both the precision and the interpretability of the fuzzy systems are improved. In order to select the best individuals from each species, we generalize the NSGA-II algorithm from one species to multi-species, and propose a new non-dominated sorting technique and collaboration mechanism for cooperative coevolutionary algorithm. Finally, the proposed approach is applied to two benchmark problems, and the results show its validity.

• Journal of Communications and Networks
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• v.18 no.1
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• pp.84-94
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• 2016

Design of effective cooperative jamming (CJ) algorithm is studied in this paper to maximize the achievable secrecy rate when the total transmit power of the source and multiple trusted terminals is constrained. Recently, the same problem was studied in [1] and an optimal algorithm was proposed involving a one-dimensional exhaustive searching.However, the computational complexity of such exhaustive searching could be very high, which may limit the practical use of the optimal algorithm. We propose an asymptotically optimal algorithm, involving only a fast line searching, which can guarantee to achieve the global optimality when the total transmit power goes to infinity. Numerical results demonstrate that the proposed asymptotically optimal algorithm essentially gives the same performance as the algorithm in [1, (44)] but with much lower computational complexity.

• International Journal of Control, Automation, and Systems
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• v.4 no.6
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• pp.725-735
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• 2006

This paper deals with the problem of self-learning cooperative motion control for the pushing task of a humanoid robot in the sagittal plane. A model with 27 linked rigid bodies is developed to simulate the system dynamics. A simple genetic algorithm(SGA) is used to find the cooperative motion, which is to minimize the total energy consumption for the entire humanoid robot body. And the multi-layer neural network based on backpropagation(BP) is also constructed and applied to generalize parameters, which are obtained from the optimization procedure by SGA, in order to control the system.

• ETRI Journal
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• v.31 no.1
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• pp.89-91
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• 2009

This letter considers the problem of resource sharing among a relay and multiple user nodes in cooperative transmission networks. We formulate this problem as a sellers' market competition and use a noncooperative game to jointly consider the benefits of the relay and the users. We also develop a distributed algorithm to search the Nash equilibrium, the solution of the game. The convergence of the proposed algorithm is analyzed. Simulation results demonstrate that the proposed game can stimulate cooperative diversity among the selfish user nodes and coordinate resource allocation among the user nodes effectively.

• International Journal of Control, Automation, and Systems
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• v.3 no.1
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• pp.1-14
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• 2005

This paper presents an obstacle detection algorithm based on the consecutive and the cooperative range sensor scanning schemes. For a known environment, a mobile robot scans the surroundings using a range sensor that can rotate 3600°. The environment is rebuilt using nodes of two adjacent walls. The robot configuration is then estimated and an obstacle is detected by comparing characteristic points of the sensor readings. In order to extract edges from noisy and inaccurate sensor readings, a filtering algorithm is developed. For multiple robot localization, a cooperative scanning method with sensor range limit is developed. Both are verified with simulation and experiments.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.8
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• pp.84-91
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• 1997

The range image is divided into surface regions which are homogeneous in their intrinsic properties. In this paper, we use cooperative relaxational algorithm between curvatures to escape local minima and choose optimal possibility to reserve edge. Cooperative relaxational algorithm between curvatures is relaxation process in which weights of center pixel;s and neighbor pixel's possiblility are determined adaptively by using deviation of curvatures. Experimental resutls show that the proposed method segments and classifies the range images more accurately compared to the other relational algorithms.

• Journal of information and communication convergence engineering
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• v.19 no.3
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• pp.155-160
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• 2021

Differential evolution is an efficient algorithm for solving continuous optimization problems. However, its performance deteriorates rapidly, and the runtime increases exponentially when differential evolution is applied for solving large-scale optimization problems. Hence, a novel cooperative coevolution differential evolution based on Spark (known as SparkDECC) is proposed. The divide-and-conquer strategy is used in SparkDECC. First, the large-scale problem is decomposed into several low-dimensional subproblems using the random grouping strategy. Subsequently, each subproblem can be addressed in a parallel manner by exploiting the parallel computation capability of the resilient distributed datasets model in Spark. Finally, the optimal solution of the entire problem is obtained using the cooperation mechanism. The experimental results on 13 high-benchmark functions show that the new algorithm performs well in terms of speedup and scalability. The effectiveness and applicability of the proposed algorithm are verified.

• Journal of the Korea Society of Computer and Information
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• v.24 no.7
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• pp.79-85
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• 2019

We investigate the D2D utility maximization in the cellular system. We focus on the non cooperative game theoretic approach to maximize the individual utility. Cellular system's perspective, interference from the D2D links must be limited to protect the cellular users. To accommodate this interference issue, utility function is first defined to control the individual D2D user's transmit power. More specifically, utility function includes the pricing which limits the individual D2D user's transmit power. Then, non cooperative power game is formulated to maximize the individual utility. Distributed algorithm is proposed to maximize the individual utility, while limiting the interference. Convergence of the proposed distributed algorithm is verified through computer simulation. Also the effect of pricing factor to SIR and interference is provided to show the performance of the proposed distributed algorithm.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.2
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• pp.136-144
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• 2010

This paper presents an algorithm for planning waypoints for the operation of a surveillance mission using cooperative unmanned aerial vehicles (UAVs) in a given map. This algorithm is rather simple and intuitive; therefore, this algorithm is easily applied to actual scenarios as well as easily handled by operators. It is assumed that UAVs do not possess complete information about targets; therefore, kinematics, intelligence, and so forth of the targets are not considered when the algorithm is in operation. This assumption is reasonable since the algorithm is solely focused on a surveillance mission. Various parameters are introduced to make the algorithm flexible and adjustable. They are related to various cost functions, which is the main idea of this algorithm. These cost functions consist of certainty of map, waypoints of co-worker UAVs, their own current positions, and a level of interest. Each cost function is formed by simple and intuitive equations, and features are handled using the aforementioned parameters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1111-1130
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• 2016

Cooperative communications can significantly improve the wireless transmission performance with the help of relay nodes. In cooperative communication networks, relay selection and power allocation are two key issues. In this paper, we propose a relay selection and power allocation scheme RS-PA-PSACO (Relay Selection-Power Allocation-Particle Swarm Ant Colony Optimization) based on PSACO (Particle Swarm Ant Colony Optimization) algorithm. This scheme can effectively reduce the computational complexity and select the optimal relay nodes. As one of the swarm intelligence algorithms, PSACO which combined both PSO (Particle Swarm Optimization) and ACO (Ant Colony Optimization) algorithms is effective to solve non-linear optimization problems through a fast global search at a low cost. The proposed RS-PA-PSACO algorithm can simultaneously obtain the optimal solutions of relay selection and power allocation to minimize the SER (Symbol Error Rate) with a fixed total power constraint both in AF (Amplify and Forward) and DF (Decode and Forward) modes. Simulation results show that the proposed scheme improves the system performance significantly both in reliability and power efficiency at a low complexity.