Multi-objective Optimization in Partner Selection

Improved random selection strategy, self-adaptive crossover and mutation operators, population co-evolution algorithm are adopted in this paper to set up a new genetic algorithm(GA), guaranteeing the universality of population, the thoroughness of solution and the execution efficiency of algorithm in solving multi-process, process crossover, work-piece flow and other condition based scheduling problem of flexible manufacturing system(FMS). The problem of GA in solving FMS scheduling is summarized in this paper, reasons and countermeasures for GA's pre-maturing are studied, longitudinal and transverse comprehensive state-based correlative self-adaptive crossover and mutation operators are raised according to the analysis on GA to avoid local optimization and pre-maturity, the co-evolutionary GA is designed to solve the scheduling model for FMS, and the algorithm is validated for the scheduling of box-type parts for precise machine tools.

Partner selection is one of the core problems in the phase of virtual enterprise (VE) creation, since the selection of right partners is crucial to the success of VE. To effectively solve the partner selection and optimization problem in VE practice, a mathematical model with the objective of minimizing the total manufacturing cost of tasks within the due date is discussed in this paper. As the objective formulation is not continuous and differential, it cannot be solved exactly by integer programming, an improved ant colony optimization algorithm (IACO) is proposed to solve the problem. A crossover operator which is usually used in genetic algorithm (GA) is introduced into IACO, so it can improve the search ability of ant colony and make consequently solution better. Finally, an illustrative example is presented to show the efficiency of the algorithm.

Virtual enterprise is the outcome of fierce competition in the world market. The selection of partners is one of the key factors for the success of virtual enterprises. Multi-agent system is an incompact agent federation composed of many agents, with its characteristics identical to virtual enterprises. It is, therefore, reasonable and effective to apply to the selection of enterprises' partner. The issue of trust is a hot topic in multi-agent system. The present trust model cannot well solve the problem of identifying the initial trust value of applicant agents. Moreover, there is room for improvement as to the calculation of agent trust value. This study puts forward a new trust management model based on multi-agent system. In the model, a fuzzy algorithm is used to calculate the initial value of the agent instead of a static definition. In addition, an independent validation module is used to test information reliability. Therefore, the problems of identifying initial trust value can be solved. Through system simulation, it is noted that based on the trust management model the cooperation rate has been enhanced and individual agent's income has risen by a big margin.

Quadratic approximation based hybrid genetic algorithm for function optimization

BackgroundMining epistatic loci which affects specific phenotypic traits is an important research issue in the field of biology. Bayesian network (BN) is a graphical model which can express the relationship between genetic loci and phenotype. Until now, it has been widely used into epistasis mining in many research work. However, this method has two disadvantages: low learning efficiency and easy to fall into local optimum. Genetic algorithm has the excellence of rapid global search and avoiding falling into local optimum. It is scalable and easy to integrate with other algorithms. This work proposes an epistasis mining approach based on genetic tabu algorithm and Bayesian network (Epi-GTBN). It uses genetic algorithm into the heuristic search strategy of Bayesian network. The individual structure can be evolved through the genetic operations of selection, crossover and mutation. It can help to find the optimal network structure, and then further to mine the epistasis loci effectively. In order to enhance the diversity of the population and obtain a more effective global optimal solution, we use the tabu search strategy into the operations of crossover and mutation in genetic algorithm. It can help to accelerate the convergence of the algorithm.ResultsWe compared Epi-GTBN with other recent algorithms using both simulated and real datasets. The experimental results demonstrate that our method has much better epistasis detection accuracy in the case of not affecting the efficiency for different datasets.ConclusionsThe presented methodology (Epi-GTBN) is an effective method for epistasis detection, and it can be seen as an interesting addition to the arsenal used in complex traits analyses.

In this paper, we propose an algorithm to generate optimal trajectory path considering the complex operating environment of excavator front part composed of the boom, arm, and bucket by using genetic algorithm. In order to express motion in space, we propose a method of coordinate plane space of grid cell, and define the fitness value by path distance. After generating chromosome candidates for each motion unit based on the polygonal structure of the front part of the excavator, we calculate the fitness value about each chromosome. The crossover and mutation operations between the chromosomes selected through roulette wheel of top 20% are repeatedly performed to generate paths with optimal fitness values. This paper verifies the structural analysis of the front part of excavator and the utility of the genetic algorithm to optimize the path in the grid space.

University examination timetabling problem is a complicated, multi-constraint combinatorial optimization problem. The current grouping genetic algorithm could get a better optimistic solution. Although the block encoding technique was applied in, the probabilities of crossover and mutation operation were still based on simple genetic algorithm. So the universality of this algorithm is not very well because the same probabilities of crossover and mutation operation could obtain different effects in different cases of examination arrangements. To find a more general algorithm, an Improved Adaptive Genetic Algorithm (IAGA) for practical applications was presented in this paper. IAGA can reconstruct the probabilities of crossover and mutation operation according to different cases of examination arrangements. To make further improvement of its convergence speed and overcome premature problem, IAGA appropriately adjusts mutation operation by Population's Maturity in addition. Moreover, the Memory Operator was added into the Algorithm to ensure getting the global optimization solution. Finally, IAGA was tested in function optimization and Examination Timetabling problems, and the experiments show that the results are promoted very well.

A novel adaptive genetic algorithm for global optimization of mathematical test functions and real-world problems

Bus scheduling problem is vital to ensure service quality and save operational cost. Electric bus scheduling problem is difficult to solve because of vehicles' limited driving range and charging requirements. This article investigates the multi-depot electric bus scheduling problem (MD-EBSP). A genetic algorithm with trip-adjustment strategy (GA-TAS) is proposed for the MD-EBSP. Firstly, a genetic algorithm (GA) with three crossover operators and seven mutation operators is devised to find a set of solutions. In each generation, a randomly selected crossover (mutation) operator is used to update the individuals. The solutions found by the GA are further improved by a trip-adjustment strategy (TAS) to obtain the final solution. The GA-TAS is compared with an adaptive large neighbourhood search method, a heuristic procedure, and experience-based scheduling schemes on seven problem instances. Experiments show that the GA-TAS outperforms comparative approaches on a number of vehicle and balancing vehicle driving tasks.

Aiming at prematureness, slow convergence rate and reduction in diversity which exist in Genetic Algorithm (GA), this paper presents Adaptive Immune Genetic Algorithm (AIGA) based on GA and immune system mechanism. Adaptive Immune Genetic Algorithm introduces antigens recognition function, immune memory function and antibodies self-adjusting function to Genetic Algorithm, and replaces the fixed probability crossover and mutation operator of Genetic Algorithm with the adaptive probability crossover and mutation operator. AIGA overcomes some disadvantages of GA, such as prematureness, slow convergence speed and reduction in diversity. And AIGA has strong global optimization ability and high searching efficiency. Then AIGA is used to optimize PID parameter for the main steam temperature control system. The simulation comparison experiment with different methods shows that PID parameters obtained by AIGA may provide better control effect than those obtained by GA and the engineering tuning methods. That is, the system control effect adopting AIGA-PID parameter has small overshoot, short adjusting time, and smooth transition. The simulation result also proves the validity of AIGA.

Because of the rapid growth of air traffic, optimizing airport management is becoming necessary in order to improveairport's capacity and better align its resources to the received traffic. In this paper we study the assignment of the arriving aircrafts to the available gates using the fixed daily schedule. We introduce a new approach based on Genetic Algorithms (GA) to solve the gate assignment problem (GAP). The encoding strategy consists in representing the chromosome by a vector of integers. The index of each gene represents the flight number and its value represents the gate to which the flight will be assigned. The method used to generate the initial population is based on three different heuristics and a random sorting of the gates. The selection method is the “In fitness proportionate selection” known as “roulette wheel selection”. In addition to one point and two point Crossover operators, we designed a Greedy procedure Crossover (GPX) operator. The experimentation is based on the use of fictive scenarios generated in accordance with the physical characteristics of the Tunis Carthage Airport and using different flight schedules. The comparison between deterministic approach, simple heuristics and the GA has shown the efficiency of the last approach in terms of solution's quality when we aim at solving the problems of large size. In order to determine the best configuration of the GA, we compared the different crossover operators and we noticed that the use of GPX improves the speed of convergence of the algorithm towards better solutions.

The schema theorem provides theoretical background for the effectiveness of genetic algorithms and serves as a formal model to explain their success. It describes the functionality of genetic algorithms under very restrictive limitations of a canonical genetic algorithm which applies a binary alphabet, individuals of equal length, fitness-proportional selection, single-point crossover, and gene-wise mutation. Applications of genetic algorithms, however, are often based on noncanonical variations and, therefore, are not verified by the theory of the traditional theorem. This paper describes the adaption of the theorem for various other crossover and mutation operators focusing on the application of genetic algorithms to a music segmentation problem.

Making Artificial Brains: Components, Topology, and Optimization.

Internet of Things (IoT) includes numerous sensing nodes that constitute a large scale-free network. Optimizing the network topology for increased resistance against malicious attacks is an NP-hard problem. Heuristic algorithms, particularly genetic algorithms, can effectively cope with such problems. However, conventional genetic algorithms are prone to falling into premature convergence owing to the lack of global search ability caused by the loss of population diversity during evolution. Although this can be alleviated by increasing population size, additional computational overhead will be incurred. Moreover, after crossover and mutation operations, individual changes in the population are mixed, and loss of optimal individuals may occur, which will slow down the evolution of the population. Therefore, we combine the population state with the evolutionary process and propose an Adaptive Robustness Evolution Algorithm (AREA) with self-competition for scale-free IoT topologies. In AREA, the crossover and mutation operations are dynamically adjusted according to population diversity to ensure global search ability. Moreover, a self-competitive mechanism is used to ensure convergence. The simulation results demonstrate that AREA is more effective in improving the robustness of scale-free IoT networks than several existing methods.

With the development of science and technology, there has been growing the grid size, the growing demand for electricity and the extent of increasing the electricity market.In this paper, a mathematical morphology and genetic algorithm based dynamic reactive power optimization method for distribution network is proposed.By means of constructing mathematical morphology filter, the binary image composed by allelic genes of chromosome is filtered and the problem that deals with the action times of voltage regulating devices for compensation is turned into filtering problem of discrete binary image.With further automate scheduling, distribution automation and unmanned substation reactive power optimization of distribution line running made an urgent request.Reactive power optimization can improve the voltage passing rate, increasing the reliability of the system, the power quality, and the system runs the security and economy of the perfect combination, very broad application prospects.