Earticle

The major task for cognitive radio (CR) systems is to sense the spectrum holes reliably and make the secondary user share the spectrum efficiently and effectively with the primary users. The Cognitive Radio cannot sense the primary users correctly because of the shadowing and fading environment in the multipath channel. To overcome this sensing problem collaboration between the secondary users has to be established. In collaborative sensing, a fusion centre will receive information from each secondary user and a decision will be made by fusion rule. Genetic algorithm is used to select secondary users having higher SNRs. MATLAB is used to carry out the simulation to obtain the optimum number of users can involved in the collaboration. HDL is implemented on Genetic algorithm to detect the optimal secondary users. Simulation and synthesis report is given in this paper.

본 연구에서는 제품의 공급지, 유통센터 및 수요지의 3 계층으로 이루어진 공급사슬 상에서 각 공급지와 각 유통센터간의 1단계 수송비용, 각 유통센터와 각수요지 사이의 2단계 수송비용 및 유통센터의 운영비용의 합을 최소화할 수 있는 제품의 수송계획 문제를 대상으로 한다. 본 연구에서는 유전 알고리즘의 선행 연구를 바탕으로 이 문제에 대하여 우수한 해를 생성할 수 있는 협력적 공진화 알고리즘을 다음과 같이 설계한다. 먼저 이 문제를 2개의 부분문제로 분할하고, 각 부분문제에 대하여 우선순위 기반의 표현을 사용한 염색체 개체들로써 모집단을 구성한다. 그리고 두 모집단내 각 개체에 대한 적합도의 평가 원리를 소개하고, 사용할 적합도 함수를 설정하고, 각 개체에 대한 적합도 평가 알고리즘을 제시한다. 마지막으로 두 모집단의 세대교체에 사용될 선택, 교배 및 돌연변이 연산자를 선정한다. 이러한 설계를 기반으로 만들어진 협력적 공진화 알고리즘을 기존의 유전 알고리즘과 그 성능을 비교해 보기 위하여 여러 개의 테스트 문제에 대하여 반복 실험한다. 그 결과, 협력적 공진화 알고리즘은 문제의 크기가 커짐에 따라 기존의 유전 알고리즘에 비해 해의 평균적인 품질이나 해의 변동성 면에서 상대적으로 더 우수한 해를 얻을 수 있었다. 본 연구에서 제시한 협력적 공진화 알고리즘 기반의 해법은 현실적인 다단계 수송문제에도 확장 적용될 수 있다.

We consider a two stage transportation problem in which the objective is to minimize the total cost including shipping cost from plants to distribution centers, shipping cost from distribution centers to customers, and the opening costs of distribution centers in a three tiers of supply chain consisting of plants, distribution centers and customers. We design a cooperative coevolutionary algorithm to solve the problem as follows. First, the problem is decomposed into two different subproblems. In each subproblem we construct a population of chromosome individuals, each of which is denoting a priority and is represented as a permutation. Then we introduce the evaluation principle for a given individual, set the evaluation function, and suggest the evaluation algorithm. Finally we set selection operator, crossover operator, and mutation operator, each of which is used to generate individuals to be included in the next generation. An experiment study is carried out to compare the performance of our cooperative coevolutionary algorithm with that of the genetic algorithm from previous study. In this experiment we obtained the result that in general our coevolutionary algorithm generates better solution than genetic algorithm as the problem size get larger, in term of both the average quality of solutions and the variance of solutions.

본 논문에서는 인력이 접근하기 힘든 오염지역 감시를 위하여 다수 무인기 기반 이동센서네트워크를 고려한 다. 개별 무인기의 센싱 범위는 제한되어 있으므로 효과적인 지역 감시를 위해서는 무인기가 서로 협력하여 효과적인 센싱 커버리지를 획득하고 보다 많은 유해물질이 검출되는 지점으로 이동할 수 있어야 한다. 본 논문에서는 센싱 및 통신 거리의 제약을 극복하기 위하여 가상력 기반의 알고리즘을 이용하는 감시 네트워크를 제안하고 테스트베드를 구 축한다. 감시 네트워크에서 각 무인기는 이웃 무인기의 센싱값과 위치 정보를 바탕으로 최적 커버리지를 획득하고 감 시 지역의 센싱 최대치 지점으로 이동하게 된다. 야외 테스트베드를 이용한 시험을 통해 제안하는 유해 물질 감시 센 서 네트워크는 오염 지역에 자발적 접근이 가능하고 높은 가중 커버리지(Weighted Coverage) 획득이 가능함을 보인다.

In this paper, we consider a mobile sensor network for monitoring a polluted area where human beings cannot access. Due to the limited sensing range of individual unmanned vehicles, they need to cooperate to achieve an effective sensing coverage and move to a more polluted region. In order to address the limitations of sensing and communication ranges, we propose a hazardous substance monitoring network based on virtual force algorithms, and develop a testbed. In the considered monitoring network, each unmanned vehicle achieves an optimal coverage and move to the highest interest area based on neighboring nodes sensing values and locations. By using experiments based on the developed testbed, we show that the proposed monitoring network can autonomously move toward a more polluted area and obtain a high weighted coverage.

Considering production and marketing coordination between supply chain enterprises, this paper specifies a multi-agent supply chain concurrent negotiation model for two-stage supply chain in the supply chain environment. Coordinator is used to search optimal proposals based on particle swarm optimization (PSO) and send these proposals to other threads. Proposal strategies based on retention value and time are put forward by learning retention value of rivals through Parzen-window estimation. Simulation experiment is conducted to test negotiation performance of the model. Compared with ordinary models, the model makes two improvements as follows: (1)PSO is inserted into coordinator to improve negotiation efficiency; (2) Proposal strategies are effectively supported by Parzen-window estimation and enable agents to consider the retention values of rivals. Further researches need to be done to study trust relationship between agents and influence of external environment on supply chain negotiation solution.

Considering that the existing artificial bee colony (ABC) algorithm can not give simultaneously attention to evolution speed and solution quality, an improved ABC algorithm is proposed based on nelder-mead simplex method (NMSM) in this paper, and defined ad NMSM-ABC. In the process of iteration, the algorithm periodically passes the best individual vertex from NMSM operator into ABC, or migrates the optimal food source information from ABC to NMSM can get away from local minimum with aid of ABC. Hence, the proposed algorithm can realize cooperative co-evolution of the two so that the desired properties are obtained. In addition, the sensitivity analysis of the key parameter of NMSM-ABC is also conducted and the best value is suggested. Finally, Numerical experiments and comparisons on 6 benchmark functions are conducted with other ABC algorithms, and the results indicate that the proposed algorithm effectively overcomes the local minimum, and dramatically enhances the global searching ability and convergence speed, and is a good cooperative co-evolution algorithm.

The scalability performance of the traditional evolutionary algorithms (EAs) deteriorates rapidly as the dimensionality of the optimization problems increases. Therefore, cooperative coevolutionary (CC) framework is proposed to overcome the defect. Different from existing CC algorithms, a novel self-adaptive based cooperative coevolutionary (SaCC) algorithm is presented in this paper. The SaCC employs three algorithms which with self-adaptive mechanism as sub-algorithms. The focus of this paper is on investigating two different cooperative coevolutionary manners. In the first manner, SaCC executes its sub-algorithms in parallel during evolve process and the corresponding algorithm is termed as SaCC-M1. In the second manner, SaCC executes its sub-algorithms in serial and the corresponding algorithm is termed as SaCC-M2. 26 test functions with 1000 dimensionalities are employed to verify the validity of SaCC-M1 and SaCC-M2. Experiment results demonstrate that SaCC-M2 outperforms its sub-algorithms and SaCC-M1. Besides, the results indicate that serial manner is another simple yet efficient manner for CC algorithms to solve large-scale global optimization problems.

The existence of malicious secondary users will bring damage to the performance of cooperative spectrum sensing and spectrum access in cognitive radio networks, so effectively curbing malicious behavior of secondary users become the key to success for cooperative spectrum sensing mechanism. Inspired by economics of repeated game theory, a trust game model and algorithm of distributed cooperative spectrum sensing oriented to malicious secondary users named FRTrust is proposed. In FRTrust, the reputation status is used to describe the performance of a secondary user in cooperative spectrum sensing process. It encourages secondary users to choose positive and honest behavior strategies for greater and long term benefits. Simultaneously, a ‘differentiation’ punishment mechanism is presented to respond malicious behaviors of secondary users. By this way, the ‘first offender’ secondary users and the ‘recidivist' secondary users can be distinguished effectively. If a secondary user departs from the normal times in its life period, it will be punished superimposed. Simulation results show that the FRTrust algorithm can encourage secondary users to participate in spectrum sensing in a cooperative attitude, improve the transaction success ratio of cooperative spectrum sensing, and guarantee the fairness and spectrum access performance for cognitive radio networks.

Energy detection performance in the signal to noise ratio (SNR) fluctuation needs to be improved. In order to obtain the highest probability of detection, a new cooperative spectrum sensing algorithm is proposed. Dynamic double energy thresholds and adaptive grid search are utilized to obtain the highest probability detection. Double thresholds are adaptive to noise fluctuation, and in order to obtain the best sensing performance, adaptive grid search is used firstly to find the optimal double thresholds. Simulation results show that the proposed algorithm has excellent robustness to noise fluctuation and good sensing performance even under the low signal to noise ratio.

Bacterial Foraging Optimization (BFO) is a novel optimization algorithm based on the social foraging behavior of E. coli bacteria, but it is difficult to optimize to get a high precision due to the randomness of the bacterial behavior, which belongs to intelligence algorithm. This paper presents an extended BFO algorithm, namely the Cooperative Bacterial Foraging Optimization (CBFO), which significantly improves the original BFO in solving clustering problems. A novel clustering method based on the CBFO could be used for solving clustering problems. In this work, firstly, The efficiency and performance of the CBFO algorithm was evaluated using six widely-used benchmark functions, coming up with comparative results produced by BFO, then Particle Swarm Optimization (PSO) is studied. Secondly, the algorithm with CBFO algorithms is used for data clustering on several benchmark data sets. The performance of the algorithm based on CBFO is compared with BFO algorithms on clustering problem. The simulation results show that the proposed CBFO outperforms the other three algorithms in terms of accuracy, robustness and convergence speed.

This paper built a optimization model and proposed an improved firefly optimization algorithm called CFA, which is based on firefly Cooperative. The main idea of CFA is to extend the single population FA to the interacting multi-swarms by cooperative Models. In this work, firstly, CFA algorithm is used for optimizing six widely-used benchmark functions and the comparative results produced by, firefly optimization algorithm(FA) are studied. Secondly, CFA algorithm used in data mining, clustering analysis on several typical data sets. The performance of typical data clustering results showed that the biological heuristic algorithm based on clustering analysis algorithm with the existing success of FA compared to faster convergence, and the clustering of higher quality.

As for multi-uninhabited aerial vehicle cooperative fire fighting problem, a multi-uninhabited aerial vehicle cooperative control method was proposed based on consensus algorithm. multi-uninhabited aerial vehicle cooperative control problem was divided into two steps: 1) a multi-uninhabited aerial vehicle arrival simultaneous control strategy under influence of wind field based on consensus algorithm was presented, which achieves the target of multi-uninhabited aerial vehicle arrival simultaneous at fire front and scattered around the fire front, and prepared for cooperative fire fighting; 2) Applying consensus algorithm, cooperative tracking of the designed uninhabited aerial vehicle motional orbit, and fighting fire from outside to inside to avoid recrudescence. In the process of multi-uninhabited aerial vehicle tracking fire front and fire fighting, both step considered the influence of wind, achieved multi-UAV fighting fire simultaneous, shortened fire fighting time to decreased fire loss to minimum. Simulation results demonstrated that the proposed method could achieve target of multi-uninhabited aerial vehicle fire fighting effectively.

Most work on cooperative diversity has assumed that the cooperating group (source and partners) and the associated average channel conditions between terminals (source, partners, and destination) are predetermined. In practical situations, however, it is important to develop the efficient algorithms for assigning the terminals with good inter-user channels for cooperating groups. In this paper, we propose the partner assignment algorithm for cooperative diversity in mobile communication systems. The proposed partner assignment algorithm is investigated by using the path loss model for mobile communication systems. Numerical results show that the proposed partner assignment algorithm provides the comparable probability of cooperative transmission to the partner assignment algorithm using exhaustive search. The probability of cooperative transmission increases with the number of users, which gives potential benefits of practical implementation to user cooperation in mobile communication systems.

In multi robot environment, the action selection strategy is important for the cooperation and coordination of multi agents. However the overlap of actions selected individually by each robot makes the acquisition of cooperation behaviors less efficient. In addition to that, a complex and dynamic environment makes cooperation even more difficult. So in this paper, we propose a control algorithm which enables each robot to determine the action for the effective cooperation in multi-robot system. Here, we propose cooperative algorithm with reinforcement learning to determine the action selection In this paper, when the environment changes, each robot selects an appropriate behavior strategy intelligently. We employ ...

Passive velocity field control (PVFC) was previously developed for fully mechanical systems, in which the motion task was specified behaviorally in terms of a velocity field, and the closed-loop was passive with respect to a supply rate given by the environment input. However the PVFC was only applied to a single manipulator, the proposed control law was derived geometrically, and the geometric and robustness properties of the closed-loop system were also analyzed. In this paper, we propose a method to apply a decentralized control algorithm to cooperative 3-wheeled mobile robots whose subsystem is under nonholonomic constraints and which convey a common rigid object in a horizontal plain. Moreover it is shown that multiple robot systems ensure stability and the velocities of augmented systems convergence to a scaled multiple of each desired velocity field for cooperative mobile robot systems.

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.