A Coevolutionary Model Based on Dynamic Combination of Genetic Algorithm and Ant Colony Algorithm

This paper transformed the process of Chinese question answering into agent coalition formation first, and then got the solution by using of combination of genetic algorithm and ant colony algorithm. The idea and routine of the algorithm were given. Coding scheme, selecting scheme, crossover operator, mutation operator and so on of genetic algorithm which suitable for Chinese question answering agent coalition were designed. The basic ant colony algorithm was improved. The experiment showed that the algorithm could accelerate the convergence rate and improve the ability of searching an optimum solution, avoid falling into local optimums.

The crowd-creation space is a manifestation of the development of innovation theory to a certain stage. With the creation of the crowd-creation space, the problem of optimizing the resource allocation of the crowd-creation space has become a research hotspot. The emergence of cloud computing provides a new idea for solving the problem of resource allocation. Common cloud computing resource allocation algorithms include genetic algorithms, simulated annealing algorithms, and ant colony algorithms. These algorithms have their obvious shortcomings, which are not conducive to solving the problem of optimal resource allocation for crowd-creation space computing. Based on this, this paper proposes an In the cloud computing environment, the algorithm for optimizing resource allocation for crowd-creation space computing adopts a combination of genetic algorithm and ant colony algorithm and optimizes it by citing some mechanisms of simulated annealing algorithm. The algorithm in this paper is an improved genetic ant colony algorithm (HGAACO). In this paper, the feasibility of the algorithm is verified through experiments. The experimental results show that with 20 tasks, the ant colony algorithm task allocation time is 93ms, the genetic ant colony algorithm time is 90ms, and the improved algorithm task allocation time proposed in this paper is 74ms, obviously superior. The algorithm proposed in this paper has a certain reference value for solving the creative space computing optimization resource allocation.

Airport information weak current system is the core of airport terminal, which is responsible for information interaction, power resource and energy consumption management among subsystems of airport terminal. Meanwhile, it is also responsible for main business dispatch management of entire airport terminal. Therefore, starting with basic ant colony algorithm and considering respective advantages of genetic and ant colony algorithms, optimization cycle of the two algorithms is introduced multiple times in this paper. Moreover, after each loop iteration of ant colony algorithm, optimal solution generated by ant colony algorithm will be added to genetic algorithm so that iterative speed of genetic algorithm can be accelerated. Meanwhile, solution calculated by genetic algorithm will be set to better path to update pheromone allocation in ant colony algorithm and achieve parameter adjustment. Experimental test proves that geneticant colony hybrid algorithm proposed in this paper has good convergence and solution efficiency, which can find the shortest path with suitable conditions in complex road networks.

Under the premise of using Ford algorithm to obtain the shortest path in the link map, in this paper the path adjustment algorithm which is the combination of the genetic algorithm and ant colony algorithm is introduced. And the path adjustment algorithm is used to adjust and optimize the current shortest path. The definition of the genetic algorithm control function was to control the appropriate combination opportunity of the two algorithms. Taking advantage of genetic algorithm to produce the original results, they were transformed into the initial pheromones value needed by ant colony algorithm, then ant colony algorithm to get the best results. Through numerical simulation analysis, it is further description that the path adjustment algorithm's effectiveness and necessity.

The partner selection in virtual enterprises organization is one of the key issues corporate enterprises experience nowadays. Based on the model of Ant Colony Optimization Algorithm (ACA) in virtual enterprise partner selection, in this paper, we fuse the genetic algorithm into ACA, called fusion algorithm, in order to improve the effect of the partner selection. The fusion algorithm has two steps: 1) it uses the GA to optimize the model of partner selection and takes advantages of rapid convergence of GA in initial search periods. 2) When GA search speed has become slow, the ACA takes over the search process, in which it uses the candidates produced by the GA as the seeds of pheromone used by ACA. By experimental comparison with GA optimization and ACA optimization, it shows that the fusion algorithm has performed better than the GA and ACA optimization, respectively, in both speed and accuracy under our selected numerical case. The fusion algorithm presented in this study may be applicable to similar business problems.

The mobile agent route is essentially a multi-constraint optimization problem, Genetic Algorithms has fast random global search ability, but the feedback information of the system does not use and has the problem of low efficiency of finding exact solutions, propose a genetic hybrid ant colony algorithm for WSN mobile agent route. Use of the fast random global search capabilities of genetic algorithm to find better solutions, then the better solution replaced by the initial pheromone of the ant colony algorithm, finally use the advantages of convergence speed of ant colony algorithm to find the global optimal solution for mobile agent route. Simulation results show that the algorithm can find optimal mobile agent route in a relatively short time, relative to other routing algorithms, reducing network latency and average energy consumption, improving the speed and efficiency of data transfer.

Inter-island shipment requests for rock aggregate products are served through the terminal for their own needs (TFON). The high demand for rock aggregate products causes many ships to queue up to be loaded. However, this condition is not comparable to the availability of dump trucks used to serve loading and unloading activities. This study aims to improve the performance of dump truck service in transporting rock aggregate so that the number of dump truck vehicles and optimal loading and unloading service times from the stockpile to ship at TFON are obtained. The research location was carried out at active rock mining companies in the Central Sulawesi region. The data collection method is carried out using field surveys (observations) using a time recording device by recording the process of transporting rock aggregates from the stockpile location to the ship in TFON and collecting secondary data on the demand for rock aggregates to be transported. The analysis method uses the hybrid Genetic Ant Colony Algorithm (ACO-GA) method namely a combination method between the Ant Colony Optimization algorithm and the Genetic Algorithm which aims to maximize the optimal number of trucks used in the transportation process and minimize the time in the loading and unloading process. The results showed that there had been an increase in service performance of the dump truck used in transporting rock aggregate with the longest distance of 2.3 km with a total of 5 dump trucks. The number of dump trucks of 5 units was selected because it falls within the fitness value criteria which is closest to the optimum value or equal to the value of the resources owned. Meanwhile, the optimal loading and unloading process time is in the range of 1.81–3.34 working days

Weapon-Target Assignment (WTA) problems are to find a proper assignment of weapons to targets with the objective of minimizing the expected damage of own-force asset. In this paper, a novel hybrid algorithm of ant colony optimization (ACO) and genetic algorithm is proposed to solve WTA problems. The proposed algorithm is to enhance the search performance of genetic algorithms by embedded ACO so as to have locally optimal offspring. This algorithm is successfully applied to WTA problems. From our simulations for those tested problems, the proposed algorithm has the best performance when compared to other existing search algorithms.

A new Heuristic Genetic Information based Ant Colony Genetic Algorithm (HGI-ACGA) to solve Traveling Salesman Problems(TSP) was proposed. HGI-ACGA's two sub-algorithms are Ant Colony Genetic Algorithm(ACGA) and Heuristic Genetic Information based Ant Colony Algorithm(HGI-ACA). ACGA enhances genetic algorithm's population diversity and reduces the search domain, while HGI-ACA eliminates the creation of invalid tours and also avoids depending on pheromone excessively. A combination of genetic information and pheromone leads to a significant improvement in performance. The strategy of HGI-ACGA algorithm can improve convergence rate and capacity of searching optimal solution. Experimental results show that the proposed algorithm generally exhibits a better solution and a higher rate of convergence for TSP than ACGA and ACA.

This paper presents a methodology to solve the manufacturing cell creation and the production scheduling problems for designing virtual cellular manufacturing systems (VCMSs). The objective is to minimize the total materials and components travelling distance incurred. The methodology consists of (i) a mathematical model that describes the characteristics of a VCMS and includes constraints such as delivery due dates of products, maximum capacities of resources, critical tools limitation, and (ii) an ant colony optimization (ACO) algorithm for manufacturing cell formation and production scheduling. Since the proposed ACO algorithm does not indicate a feasible schedule explicitly, two simple heuristics are developed to assign workstations to the operations of the jobs, and to construct the final schedule. To demonstrate the effectiveness of the proposed methodology, both the ACO algorithm and the genetic algorithm are applied to design manufacturing cells for a company in China producing internal combustion engine components. Comparison of the results obtained with the results supplied by the company on the existing manufacturing system show that both the ACO algorithm and the genetic algorithm together with the virtual cellular manufacturing concept perform better than the current manufacturing practice in terms of average workstation utilization, product completion time and system throughput. Also, the results of a set of randomly generated numerical experiments show that the proposed ACO algorithm generates excellent final solutions in a much shorter computation time when compared with the genetic algorithm. Therefore, the mathematical model and the ACO algorithm proposed in this paper form a simple, but effective and efficient methodology to solve the manufacturing cell creation and production scheduling problems for designing VCMSs.

Robot coalition problem is a complicated combinatorial optimization problem. In this paper, a novel quantum ant colony algorithm for the problem is proposed, where the thought of quantum evolution algorithm is combined with ant colony algorithm. In this algorithm, the pheromone is encoded by quantum bits and a new pheromone representation is designed. To avoid the algorithm falling into local optimum, the strategy of quantum crossover is designed to improve the information structure of population. Simulation results show that the algorithm is more effective than quantum genetic algorithm and basic ant colony algorithm.

Mobile robots are widely used in daily life, and ant colony algorithm is often adopted for its path planning. However, the classic ant colony algorithm has poor search ability and search speed due to the monotony of individuals within the population. In order to solve the problems of local optimal solution, slow convergence speed and low search efficiency of classic ant colony algorithm, a genetic-ant colony algorithm is proposed in this paper. The algorithm parameters are optimized by simulation experiment to determine the best parameter combination. The method is to give a certain range of random pheromone values to the primary ant individuals to enrich the population diversity. In each cycle, m ants are sent for foraging. After each foraging cycle, aiming at the growth of individual pheromone value, the fitness function of genetic algorithm is established to evaluate individual fitness value, and the parental individuals carry out genetic operations such as selection, crossover and variation to produce offspring individuals with the same number and higher pheromone level, the offspring individuals carry out the next cycle of foraging and keep cycling. The population diversity is maintained at a high level, while the increase in the level of individual pheromone speeds up the convergence and operation of the algorithm. The simulation results show that the running time of the improved algorithm is significantly shorter than that of the traditional ant colony algorithm in the 40 * 40 grid map, and the running time of the algorithm is reduced by 21.34%; in the complex grid environment, the frequency of obtaining the optimal path of the algorithm is significantly higher than that of ordinary ant colonies, and the frequency is increased; and the number of iterations when the optimal path is reached for the first time is significantly reduced,–which has verified the effectiveness, stability and superiority of genetic-ant colony algorithm.

A hybrid algorithm (HA) integrated genetic algorithm (GA) with ant colony optimization (ACO) for solving Traveling Salesman Problems(TSP) was studied to get better optimization performance than each single algorithm, and complement merits each other and avoid each own demerits. The hybrid algorithm runs GA first and then ACO. A new strategy called GSA was proposed aiming at the key link in the HA that converts genetic solution from GA into information pheromone to distribute in ACO. GSA takes new matrix which is formed by the combination of the former 90% of individual from genetic solution and 10% of individual by random generation as the basis of transformation of pheromone value. The best combination of genetic operators in GA was also discussed. Several TSP were used as simulation tests to test genetic operators matching and optimization performance of HA. The results show that PMX crossover matched with IVM mutation in the GA is the best combination of genetic operators which is able to make GA improve the precision of optimal solution, and HA using the best combination operators and GSA strategy is successful and available to search for optimal solution in high efficiency and has good convergence.

In the field of health care, the Wireless Body Area Networks(WBANs), through placed miniaturization, low power consumption of sensor nodes in patient's body or on the surface to measure a variety of physiological parameters, to monitor or diagnose the health of human body. However, in this process, WBANs nodes usually use batteries, especially for implantable flexible node it is difficult to accomplish the replacement of batteries, the energy that the node can carry is very limited, so the efficient use of energy is the most important problem to consider when designing WBANs routing algorithm. By considering the factors such as residual energy of node, the importance level of node, path cost and path energy difference ratio, this paper gives the definition of Optimal Path of Energy Consumption(OPEC) in WBANs, and The Optimal Energy Consumption Artificial Bee Colony(ABC) Based WBANs(OEABC) is designed. The performance simulation is carried out to verify the effectiveness of the OEABC, compared with Genetic Algorithm and Ant Colony Algorithm, it shows that t the OEABC has a fast convergence rate.

This paper presents a method of optimized PID parameter self-adapted ant colony algorithm with aberrance gene, based on ant colony algorithm. This method overcomes genetic algorithm’s defects of repeated iteration, slower solving efficiency, ordinary ant colony algorithm’s defects of slow convergence speed, easy to get stagnate, and low ability of full search. For a given system, the results of simulation experiments which compare to the result of Z-N optimization and evolution of genetic algorithm optimization and evolution of ant colony system optimization, it has more excellent performance in finding best solution and convergence, the PID parameters also have optimality, system possesses dynamic controlling and performance. The experiments show that this method has its practical value on controlling other objection and process.