Earticle

In the data scheduling process of wireless sensor network (WSN), node data fusion is one of the main methods to reduce network communication volume. The decrease in network communication volume contributes to the network energy consumption reduction, which is crucial for improving the WSN lifecycle. Hence, we propose an energy-saving hybrid-optimization scheduling algorithm based on fat tree (FT) for WSN, which is referred to as the FTEBHSA algorithm. In the scheduling algorithm, multiple strategies are adopted to optimize for saving energy, involving shortest path tree optimization, fusion tree load balance, a fusion node rotation mechanism, and a sleep mechanism for monitoring nodes. More importantly, we introduce the FT structure to organically integrate these strategies for reducing the energy consumption and boosting the WSN lifecycle. The simulation experiment results verify that the proposed hybrid-optimization scheduling algorithm performs optimally in optimizing the energy consumption.

The ant colony optimization (ACO) algorithm is a classical metaheuristic optimization algorithm. However, the conventional ACO was liable to trap in the local minimum and has an inherent slow rate of convergence. In this work, we propose a novel combinatorial ACO algorithm (CG-ACO) to alleviate these limitations. The genetic algorithm and the cloud model were embedded into the ACO to find better initial solutions and the optimal parameters. In the experiment section, we compared CG-ACO with the state-of-the-art methods and discussed the parameter stability of CG-ACO. The experiment results showed that the CG-ACO achieved better performance than ACO_R, simple genetic algorithm (SGA), CQPSO and CAFSA and was more likely to reach the global optimal solution.

Foraging is a biological process, where a bacterium moves to search for nutriments, and avoids harmful substances. This paper proposes a hybrid approach integrating the bacterial foraging optimization algorithm (BFOA) in a radial basis function neural network, applied to image classification, in order to improve the classification rate and the objective function value. At the beginning, the proposed approach is presented and described. Then its performance is studied with an accent on the variation of the number of bacteria in the population, the number of reproduction steps, the number of elimination-dispersal steps and the number of chemotactic steps of bacteria. By using various values of BFOA parameters, and after different tests, it is found that the proposed hybrid approach is very robust and efficient for several-image classification.

Classification is a substantial operation in data mining, and each element is distributed taking into account its feature values in the corresponding class. Metaheuristics have been widely used in attempts to solve satellite image classification problems. This article proposes a hybrid approach, the flower pigeons-inspired optimization algorithm (FPIO), and the local search method of the flower pollination algorithm is integrated into the pigeon-inspired algorithm. The efficiency and power of the proposed FPIO approach are displayed with a series of images, supported by computational results that demonstrate the cogency of the proposed classification method on satellite imagery. For this work, the Davies-Bouldin Index is used as an objective function. FPIO is applied to different types of images (synthetic, Alsat-2B, and Sentinel-2). Moreover, a comparative experiment between FPIO and the genetic algorithm genetic algorithm is conducted. Experimental results showed that GA outperformed FPIO in matters of time computing. However, FPIO provided better quality results with less confusion. The overall experimental results demonstrate that the proposed approach is an efficient method for satellite imagery classification.

Task scheduling is one of the most important issues in heterogeneous environments when high efficiency is required. Because task scheduling is a Nondeterministic Polynomial (NP)-hard problem, many evolutionary algorithms have been adopted to solve this problem. Since the convergence speed of solutions in population-based algorithms is low, they are integrated with local search algorithms. Thus, in this paper, to optimize the task scheduling makespan, a hybrid particle swarm optimization and hill climbing algorithm is proposed. The experimental results on random and scientific Directed Acyclic Graph (DAG) showed that the proposed algorithm performs effectively in terms of the makespan compared to the current well-known heuristic and particle swarm optimization algorithms.

According to Genetic algorithms principle, the new hybrid evolutionary algorithm (HEA) is proposed in this paper by combining the Immune algorithm, Genetic algorithm and Pareto optimal solutions. The HEA has high convergence precision and improved the diversity of population. Multiple near optimization paths can be developed by the algorithm with multi‐objective restriction, and satisfy to minimize the routing of transportation and the numbers of the vehicles. The HEA has been used to solve the vehicle routing problem, the results of simulation experiment show that the HEA can gain higher global convergence rate and higher speed.

Ant colony optimization (ACO) algorithm is a new heuristic algorithm which has been demonstrated a successful technology and applied to solving complex optimization problems. But the ACO exists the low solving precision and premature convergence problem, particle swarm optimization (PSO) algorithm is introduced to improve performance of the ACO algorithm. A novel hybrid optimization (HPSACO) algorithm based on combining collaborative strategy, particle swarm optimization and ant colony optimization is proposed for the traveling salesman problems in this paper. The HPSACO algorithm makes use of the exploration capability of the PSO algorithm and stochastic capability of the ACO algorithm. The main idea of the HPSACO algorithm uses the rapidity of the PSO algorithm to obtain a series of initializing optimal solutions for dynamically adjusting the initial pheromone distribution of the ACO algorithm. Then the parallel search ability of the he ACO algorithm are used to obtain the optimal solution of solving problem. Finally, various scale TSP are selected to verify the effectiveness and efficiency of the proposed HPSACO algorithm. The simulation results show that the proposed HPSACO algorithm takes on the better search precision, the faster convergence speed and avoids the stagnation phenomena.

For solving traveling salesman problem (TSP), the ant colony optimization (ACO) algorithm and simulated annealing (SA) algorithm are used to propose a two-phase hybrid optimization (TPASHO) algorithm in this paper. In proposed TPASHO algorithm, the advantages of parallel, collaborative and positive feedback of the ACO algorithm are used to implement the global search in the current temperature. And adaptive adjustment threshold strategy is used to improve the space exploration and balance the local exploitation. When the calculation process of the ACO algorithm falls into the stagnation, the SA algorithm is used to get a local optimal solution. And the obtained best solution of the ACO algorithm is regarded as the initial solution of the SA algorithm, and then a fine search is realized in the neighborhood. Finally, the probabilistic jumping property of the SA algorithm is used to effectively avoid falling into local optimal solution. In order to verify the effectiveness and efficiency of the proposed TPASHO algorithm, some typical TSP is selected to test. The simulation results show that the proposed TPASHO algorithm can effectively obtain the global optimal solution and avoid the stagnation phenomena. And it has the better search precision and the faster convergence speed.

This paper presents a hybrid algorithm for traveling salesman problem. The algorithm is a combination of the genetic algorithm and simulated annealing algorithm; in other words, it is a hybrid algorithm. The combination overcomes the deficiencies of the two algorithms when acting separately. The real distance between customers has been used on the basis of geographical information system (GIS) in order to make the result more suitable in real-life. The algorithm has tested on the examples of international standards. We made a comparison with the result of second nearest neighbor algorithm and genetic optimization algorithm. The test showed that the algorithm proposed in this paper has improved the results.

Due to the insufficient of the fuzzy neural network in solving complex problems, an improved ant colony optimization(ACO) algorithm is introduced into the fuzzy neural network in order to propose a novel hybrid optimization(APEACOFNN) algorithm in this paper. In the APEACOFNN algorithm, the self-adaptive pheromone evaporation factor strategy is used to dynamically adjust the pheromone evaporation factor on searching route in order to gradually lessen the amount of information between the optimal path and the worst path, and realize the full searching optimization for decision variable space. Then an improved ACO(APEACO) algorithm is obtained. Aiming at the parameters optimization problem of fuzzy neural network, the proposed APEACO algorithm is used to comprehensively optimize and select the parameters of fuzzy neural network in order to propose a novel hybrid optimization (APEACOFNN) algorithm. Finally, in order to test the effectiveness of the APEACOFNN algorithm, five UCI data sets are selected. The experimental results show that the proposed APEACOFNN algorithm takes the faster approximation objectives and higher solving accuracy.

In allusion to the deficiencies of the ant colony optimization algorithm for solving the complex problem, the genetic algorithm is introduced into the ant colony optimization algorithm in order to propose a novel hybrid optimization (NHGACO) algorithm in this paper. In the NHGACO algorithm, the genetic algorithm is used to update the global optimal solution and the ant colony optimization algorithm is used to dynamically balance the global search ability and local search ability in order to improve the convergence speed. Finally, some complex benchmark functions are selected to prove the validity of the proposed NHGACO algorithm. The experiment results show that the proposed NHGACO algorithm can obtain the global optimal solution and avoid the phenomena of the stagnation, and take on the fast convergence and the better robustness.

Fuzzy neural network(FNN) is a neural network based on combining the advantages of the fuzzy theory and neural network. It has the characteristics of dealing with the non-linear and fuzziness and so on. Particle swarm optimization(PSO) algorithm is a population-based search algorithm by simulating the social behavior of birds within a flock. So the quantum PSO(QPSO) algorithm is proposed for optimizing the parameters of FNN in order to construct a new hybrid optimization(QPSO-FNN) algorithm in this paper. In the proposed QPSO-FNN algorithm, the quantum theory is used to improve the PSO algorithm, then the global optimization ability of QPSO algorithm is optimize the parameters of FNN model by putting these parameters in the particle encoding. The found optimal values are regarded as the parameters of FNN model to obtain the final QPSO-FNN method. Finally, the QPSO-FNN algorithm is used to solve the complex problem, the experimental results show that the QPSO-FNN algorithm takes on the shorter response time and higher solving accuracy.

Aiming at the problem that the particle in the traditional particle swarm optimization algorithm is easy to fall into the local optimum and the convergence rate is slow, this paper proposed an improved particle swarm optimization algorithm. In particle swarm optimization algorithm, the advantages and disadvantages of the algorithm is directly decided by the performance of the particle, the paper introduced the chaos mechanism, enhance the ergodicity and particle will be quantized in the solution space, on the premise of ensuring diversity of solution, the particle get better global search ability. Meanwhile, based on the problem of slow convergence speed of the algorithm in the late, on the one hand to dynamically adjust the inertia weight of impact speed, makes the particle movement speed tend to be reasonable, on the other hand, using k-means algorithm to optimize progeny particle and get more reasonable clustering center, make the algorithm fast convergence. Experiments show that using improved Particle Swarm Optimization algorithm with high precision, strong stability and fast convergence.

A hybrid intelligent optimization algorithm based on quantum particle swarm is presented to solve the problem that the local search ability of traditional SFLA is poor and converges very slowly. The particle is quantized and introduced chaos mechanism in the algorithm in order to enhance the global search ability, using the escape strategy, the group is divided into three clusters and mutation operation on the cluster within individuals, not only improves the convergence speed and ensure the performance of the algorithm. Experiments show that the improved algorithm has the characteristics of strong optimization capability and performance is improved greatly in whether comparison of the baseline function or analysis of universal database, compared with the other two algorithms have obvious advantages.

For the premature convergence and initial pheromone distribution problem of ant colony optimization algorithm, an improved particle swarm optimization (MPSO) algorithm is introduced into ant colony optimization algorithm in order to propose a novel hybrid evolution optimization (HEACO) algorithm in this paper. In the proposed HEACO algorithm, the ergodicity of the chaos is used to initialize the swarm in order to enhance the diversity of the particle swarm, and adjust the mutation probability and inertia weighting factor in order to improve the capability of local and global search. Then the MPSO algorithm is used to control the parameters of the heuristic factor, pheromone evaporation coefficient, and the stochastic selection threshold in order to effectively overcome the parameter influences of ACO, reduce the numbers of useless experiments and balance the developing optimal solution and enlarging search space. A series of typical traveling salesman problems are selected to validity the effectiveness of the proposed HEACO algorithm. The simulation results show that the performance of the proposed HEACO algorithm is better than the traditional ACO algorithm and PSO algorithm. So the proposed HEACO algorithm can effectively improve the solving efficiency and quality, and speed up the convergence and computation.

The resource scheduling imbalance is a multi-objective optimization problem in cloud computing environment, this paper introduced the particle swarm optimization algorithm in cloud computing, a simulated annealing ideas is proposed in view of the prematurity of the algorithm, on the premise of performance determination, the position of the particle is determined by the probability choice, which helps the particle to escape. In order to enhance the global searching ability of the particle, the algorithm is combined with the chaotic mechanism to improve the accuracy of the algorithm. The inertia weight is adjusted dynamically according to the current state of the particle, accordingly, at the same time to obtain the optimal solution to ensure the convergence. Analysis of the experimental results show that the improved algorithm has a significant improvement in the ability of optimization and convergence speed, compared with other algorithms, the benchmark functions comparison is better, the different resource task proportion spent the shortest time and load balancing is the highest.

With the growing number of alternative services in the cloud environment, users have put forward new requirements to solve the service dynamic selection problem quickly and efficiently. In this paper, an evaluation model of service process which considers concurrent requests and service association is proposed. This model evaluates the service process from three dimensions which are functional quality dimension, non-functional quality dimension and transactional dimension. To solve the service selection problem efficiently, we first design a novel coding strategy of particle, and then propose an approach based on hybrid particle swarm optimization algorithm which combines the crossover and mutation operators of genetic algorithm. The experimental results show that our proposed approach is feasible and effective.

Traditional flexible flow shop scheduling cannot adapt to the work processes with existence of parallel machines, and blocks or limits the processes with no-wait constraints. Firstly, according to the problem in NWBFFSSP, which minimizes the maximum time used in the flow shop, an optimal solving model has been designed to realize the flexible flow shop scheduling with multi constraints; besides, for the distribution of machinery is improved, Finally, in the solving process, a real-time release priority strategy has been proposed to determine processing machine for each work piece. Furthermore, a methodology to detect work piece conflicts has been introduced while the conflicts are then eliminated by a kind of right moving strategy based on the maximum difference. The experimental results verify the effectiveness and feasibility of the proposed algorithm.

In cloud computing environment, there are a large number of users which lead to huge amount of tasks to be processed by system. In order to make the system complete the service requests efficiently, how to schedule the tasks becomes the focus of cloud computing Research. A task scheduling algorithm based on PSO and ACO for cloud computing is presented in this paper. First, the algorithm uses particle swarm optimization algorithm to get the initial solution quickly, and then according to this scheduling result the initial pheromone distribution of ant colony algorithm is generated. Finally, the ant colony algorithm is used to get the optimal solution of task scheduling. The experiment simulated on CloudSim platform shows that the algorithm has good effect in real-time performance and optimization capability. It is an effective task scheduling algorithm.

Normally, improving the performance of clustering depends on improvement of the algorithm. On the basis, this paper presents a hybrid strategy optimization algorithm that K-means algorithm effectively combined with PSO algorithm, which not only has played their respective advantages, but also reflected a hybrid performance. First of all, combined with a semi-supervised clustering idea， to optimize the clustering center of particle by K - means in the iteration of algorithm, enhanced the searching capability of the particles. Secondly, improved the traditional K - means enhance the ability of the algorithm to deal with the concave and convex points. Finally, the algorithm is introduced into the particle state determination mechanism, on implementing mutation for unstable particles, so that the algorithm to obtain stable performance. Experimental results show that the hybrid algorithm optimization ability is outstanding, and the convergence and stability can be effectively improved.