Study on efficient channel assignment method using the genetic algorithm for mobile communication systems

In cellular mobile communication systems, radio channels are repeatedly used in order to efficiently use assigned frequency bands. The computation time for the optimization of channel assignment increases with the increase of cell number. This paper presents a group channel assignment method for the optimization and its simulation results. This method utilizes the genetic algorithm (GA). Prior to the optimization calculation, whole channel cells are divided into subgroups and then, each subgroup is optimized. Using this result, the whole cells are further optimized. This process enables an efficient calculation to optimize the channel assignment. The results of simulation shows that the proposed method gives a shorter computation time compared with the method called as the individual assignment.     

Extensive experiments with hybrid genetic algorithms for the solution of the quadratic assignment problem

In this paper we perform extensive computational experiments solving quadratic assignment problems using various variants of a hybrid genetic algorithm. We introduce a new tabu search (simple tabu). We compared the modified robust tabu and the simple tabu as improvement algorithms in a hybrid genetic algorithm with other tabu searches (concentric tabu, ring moves, all moves, robust tabu) with superior results. We also tested several modifications of the hybrid genetic algorithm and all of them produced good results.     

The on-line first-fit algorithm for radio frequency assignment problems

The radio frequency assignment problem is to minimize the number of frequencies used by transmitters with no interference in radio communication networks; it can be modeled as the minimum vertex coloring problem on unit disk graphs. In this paper, we consider the on-line first-fit algorithm for the problem and show that the competitive ratio of the algorithm for the unit disk graph G with χ(G)=2 is 3, where χ(G) is the chromatic number of G. Moreover, the competitive ratio of the algorithm for the unit disk graph G with χ(G)>2 is at least 4−3/χ(G). The average performance for the algorithm is also discussed in this paper.     

A modified genetic algorithm for distributed scheduling problems

Genetic algorithms (GAs) have been widely applied to the scheduling and sequencing problems due to its applicability to different domains and the capability in obtaining near-optimal results. Many investigated GAs are mainly concentrated on the traditional single factory or single job-shop scheduling problems. However, with the increasing popularity of distributed, or globalized production, the previously used GAs are required to be further explored in order to deal with the newly emerged distributed scheduling problems. In this paper, a modified GA is presented, which is capable of solving traditional scheduling problems as well as distributed scheduling problems. Various scheduling objectives can be achieved including minimizing makespan, cost and weighted multiple criteria. The proposed algorithm has been evaluated with satisfactory results through several classical scheduling benchmarks. Furthermore, the capability of the modified GA was also tested for handling the distributed scheduling problems.     

Feeder assignment optimization algorithm for multi-head mounter

This paper analyzes optimization algorithms of assembly time for a multi-head mounter. The algorithm in this paper is composed of four steps. First, it assigns the components to feeders based on the "one-to-many mapping". Secondly, it assigns nozzles to heads by making full use of the "on-the-fly nozzle change" heads. Thirdly, it organizes the feeder groups so that the heads can pick and place components group by group. Finally, it assigns feeder groups to slots. The result demonstrates that the algorithm has good performance in practice.     

Feeder assignment optimization algorithm for multi-head mounter

This paper analyzes optimization algorithms of assembly time for a multi-head mounter. The algorithm in this paper is composed of four steps. First, it assigns the components to feeders based on the ＂one-to-many mapping＂. Secondly, it assigns nozzles to heads by making full use of the ＂on-the-fly nozzle change＂ heads. Thirdly, it Qrganizes the feeder groups so that the heads can pick and place components group by group. Finally, it assigns feeder groups to slots. The result demonstrates that the algorithm has good performance in practice.     

An iterative genetic algorithm for the assembly line worker assignment and balancing problem of type-II

In this study, we consider the assembly line worker assignment and balancing problem of type-II (ALWABP-2). ALWABP-2 arises when task times differ depending on operator skills and concerns with the assignment of tasks and operators to stations in order to minimize the cycle time. We developed an iterative genetic algorithm (IGA) to solve this problem. In the IGA, three search approaches are adopted in order to obtain search diversity and efficiency: modified bisection search, genetic algorithm and iterated local search. When designing the IGA, all the parameters such as construction heuristics, genetic operators and local search operators are adapted specifically to the ALWABP-2. The performance of the proposed IGA is compared with heuristic and metaheuristic approaches on benchmark problem instances. Experimental results show that the proposed IGA is very effective and robust for a large set of benchmark problems.     

A genetic algorithm calibration method based on convergence due to genetic drift

The selection of Genetic Algorithm (GA) parameters is a difficult problem, and if not addressed adequately, solutions of good quality are unlikely to be found. A number of approaches have been developed to assist in the calibration of GAs, however there does not exist an accepted method to determine the parameter values. In this paper, a GA calibration methodology is proposed based on the convergence of the population due to genetic drift, to allow suitable GA parameter values to be determined without requiring a trial-and-error approach. The proposed GA calibration method is compared to another GA calibration method, as well as typical parameter values, and is found to regularly lead the GA to better solutions, on a wide range of test functions. The simplicity and general applicability of the proposed approach allows suitable GA parameter values to be estimated for a wide range of situations.     

A genetic algorithm based robust learning credit assignment cerebellar model articulation controller

In this paper, a novel approach of genetic algorithm based robust learning credit assignment cerebellar model articulation controller (GCA-CMAC) is proposed. The cerebellar model articulation controller (CMAC) is a neurological model, which has an attractive property of learning speed. However, the distributions of errors into the addressed hypercubes of CMAC are not proportional to their credibility and may cause unacceptable learning performance. The credit assignment CMAC (CA-CMAC) can solve this problem by using the creditability of hypercubes that the calculated errors are assigned proportional to the inverse of learning times. Afterward, the obtained learning times can be optimized by genetic algorithm (GA) to increase its accuracy. In this paper, the proposed algorithm is to combine credit assignment ideas and GA to provide accurate learning for CMAC. Moreover, we embed the robust learning approach into the GCA-CMAC and dynamically adjust the learning constant for training data with noise or outliers. From simulation results, it shows that the proposed algorithm outperforms other CMACs.     

考虑分配次序的无人机协同目标分配建模与遗传算法求解

本文研究了动态战场环境中的多无人机协同目标分配(MUCTA)问题.首先通过分析无人机(UAV)分配次序对打击任务总收益的影响,设计了动态战场环境的更新规则.将航程代价和任务代价作为惩罚项修正目标函数,建立了考虑分配次序的UAVs协同目标分配优化模型.然后针对模型的物理意义改进了遗传算法基因编码方式,设计了MUCTA遗传算法.该算法利用状态转移思想,引进SDR算子获得多种分配次序种群,同时以单行变异算子修正UAV与目标对应关系,并采用最优个体法和轮盘赌法筛选子代个体.最后仿真结果验证了所设计算法的有效性.