Strong Global Search Ability Research Articles

A novel robust hybrid gravitational search algorithm for reusable launch vehicle approach and landing trajectory optimization

The approach and landing (A&L) trajectory optimization is a critical problem for secure flight of reusable launch vehicle (RLV). In this paper, the A&L is divided into two sub-phases, glide phase and flare phase respectively. The flare phase is designed firstly based on the desired touchdown (TD) states. Then, the glide phase is optimized using a proposed novel robust hybrid algorithm that combines advantages of the gravitational search algorithm (GSA) and gauss pseudospectral method (GPM). In the proposed hybrid algorithm, an improved GSA (IGSA) is presented to enhance the convergence speed and the global search ability, by adopting the elite memory reservation strategy and an adaptive gravitational constant adaption with individual optimum fitness feedback. At the beginning stage of search process, an initialization generator is constructed to find an optimum solution with IGSA, due to its strong global search ability and robustness to the initial values. When the change in fitness value satisfies the predefined value, the IGSA is replaced by the GPM to accelerate the search process and to get an accurate optimum solution. Finally, the Monte Carlo simulation results are analyzed in detail, which demonstrate the proposed method is practicable. The comparison with GSA and GPM shows that the hybrid algorithm has better performance in terms of convergence speed, robustness and accuracy for solving the RLV A&L trajectory optimization problem.

Parameter Identification of the 2-Chlorophenol Oxidation Model Using Improved Differential Search Algorithm

Parameter identification plays a crucial role for simulating and using model. This paper firstly carried out the sensitivity analysis of the 2-chlorophenol oxidation model in supercritical water using the Monte Carlo method. Then, to address the nonlinearity of the model, two improved differential search (DS) algorithms were proposed to carry out the parameter identification of the model. One strategy is to adopt the Latin hypercube sampling method to replace the uniform distribution of initial population; the other is to combine DS with simplex method. The results of sensitivity analysis reveal the sensitivity and the degree of difficulty identified for every model parameter. Furthermore, the posteriori probability distribution of parameters and the collaborative relationship between any two parameters can be obtained. To verify the effectiveness of the improved algorithms, the optimization performance of improved DS in kinetic parameter estimation is studied and compared with that of the basic DS algorithm, differential evolution, artificial bee colony optimization, and quantum-behaved particle swarm optimization. And the experimental results demonstrate that the DS with the Latin hypercube sampling method does not present better performance, while the hybrid methods have the advantages of strong global search ability and local search ability and are more effective than the other algorithms.

Application of Tabu-Genetic Algorithm in Reactive Power Optimization

By using tabu search algorithm which has strong local search ability as mutation operator of genetic algorithm, the tabu-genetic algorithm is designed for reactive power optimization in this paper, the strong global search ability of genetic algorithm and strong local search ability of tabu search algorithm is combined, the disadvantage of weak local search ability of genetic algorithm is conquered. Otherwise, the over limit of population is recorded and filtered, to ensure the final individual is under limit and effective. The tabu-genetic algorithm and simple genetic algorithm are used for simulation of IEEE 14-bus system 500 times, the results indicate that the performance of the tabu-genetic algorithm is much better than the simple genetic algorithm, its local search ability is improved obviously, and the active power loss is reduced more.

A Method of Controlled Islanding Strategy Based on Artificial Fish Swarm Algorithm

Optimal controlled islanding strategy searching is a complex combinational optimization problem. Artificial fish swarm algorithm (AFSA) based on the simulation of fish swarm is an intelligent meta-heuristics. In this paper, AFSA is applied to the searching of optimal controlled islanding strategy. In order to minimize the unbalanced power in each island, a mathematic model is established with some constraints. Simulation result on IEEE-39 node system shows that the proposed method can obtain the optimal strategy promptly and has strong global search ability.

Inversion of Oblique Ionograms Based on Hybrid Genetic Algorithm

AbstractThe oblique sounding is a powerful tool for acquiring ionospheric information, and we can get the structural parameters related to ionosphere by inversing the oblique ionogram. Genetic algorithm (GA) is a widely used inversion method which can reduce the non‐uniqueness of the inversion problems without depending on the initial estimation, but the accuracy and reliability of GA's inversion results may be decreased due to “premature convergence” and inferior local search ability. So an improved hybrid genetic algorithm (HGA) based on simulated annealing algorithm (SA) is proposed and firstly applied to inversion of oblique ionogram. HGA combines the advantages of GA and SA together and makes full use of them, so it has a strong global search ability and local search ability. To verify the reliability and stability of HGA's inversion results, we firstly use GA, SA, and HGA to inversion of the synthetic oblique ionogram and compare the inversion results of the three algorithms, find that HGA's results are nearest to the real values and its needed iterations are also much smaller than others. The inversion results include the critical frequency, the height of maximum electron density and half‐thickness. Then, comparing the influences on inversion results by changing the population size and the total iterations, we find that the HGA effectively reduces the influences. Lastly, we use the three algorithms to invert the real ionograms and compare the inversion results with the vertical data in the midpoint of transmitting and receiving site. The results show that the stability and the searching optimal solution ability of HGA are obviously better than the GA and SA, and its inversion results are reliable, so HGA has strong referential significance and practical value for the inversion of real ionograms.

A heuristic search algorithm for the multiple measurement vectors problem

In this paper, we address the multiple measurement vectors problem, which is now a hot topic in the compressed sensing theory and its various applications. We propose a novel heuristic search algorithm called HSAMMV to solve the problem, which is modeled as a combinatorial optimization. HSAMMV is proposed in the framework of simulated annealing algorithm. The main innovation is to take advantage of some greedy pursuit algorithms for designing the initial solution and the generating mechanism of HSAMMV. Compared with some state-of-the-art algorithms, the numerical simulation results illustrate that HSAMMV has strong global search ability and quite good recovery performance.

Boiler combustion efficiency optimization based on improved radial basis neural network

In order to improve the training accuracy of radial basis neural network,this paper proposed a hybrid optimization algorithm.The algorithm used the strong global search ability of Particle Swarm Optimization(PSO) algorithm to avoid the adverse effect by choosing initial point in the K-means algorithm,thus improving the network center search speed.Meanwhile,the dynamic weight algorithm was used to avoid the ill-posed problem,and to further improve the network approximation ability.The boiler combustion instance indicates that the improved algorithm is efficient and practical.

A Fast and Efficient Genetic Evolution Algorithm

This study presents an improved genetic algorithm. The algorithm introduced acceleration operator in the traditional genetic algorithm, effectively reducing the computational complexity. The search speed of the algorithm has been greatly improved, so that it can quickly find the global optimal solution. The accelerating collaborative operator lessons from the thoughts of binary search algorithm combining with the variable step length strategy. The accelerating operator has strong local search ability and crossover and mutation operators have strong global search ability, then combining these operators generates a new Genetic algorithm. The tests on the different functions show that the improved algorithm has the advantages of faster convergence and higher stability in the case of a small population than traditional genetic algorithm and can effectively avoid the premature phenomenon.

A Hybrid Algorithm for Community Detection Using PSO and EO

Community detection in networks is one of the most prominent areas of network science which is very hard and not yet satisfactorily solved. A hybrid algorithm based on particle swarm optimization (PSO) and Extremal Optimization (EO) for community detection is. PSO algorithm has strong global search ability but is easily to trap into the local optima, while EO algorithm can make the search to jump out of local optima due to its strong local search ability. A special encoding scheme based on the partition solution of a network is designed which can automatically determine the number of the community in a network. The popular modularity Q is used as the fitness of PSO algorithm and node betweenness centrality is adopted as the fitness of EO algorithm. EO algorithm can repair the isolated nodes existing in the particles and make the partition result more precise. Several experiments in real networks demonstrate that the algorithm obtains high modularity and achieves good community results.

Improved Immune Clonal Selection Algorithm and its Application in Power Network Planning

Power network planning is a NP hard problem difficult to be solved. It can be contributed to similar TSP problem. Aiming at the slow convergence speed of the traditional immune clonal selection algorithm (ICA), adaptive immune clonal selection algorithm without memory（AICA）and adaptive immune clonal selection algorithm with memory(AICAM) are proposed respectively based on the combination of adaptive algorithm of clonal probability, immune probability , and group disaster algorithm. The two proposed algorithms have been applied to Power network planning problem. The adaptive algorithm has strong global search ability and weak local search ability at early evolution. Global search ability is weakened and local search ability is enhanced with the process of evolution in order to find global optimal point. The application of group disaster algorithm can enhance the diversity of the population and avoid the premature problems to some extent. Simulation results indicate that compared with the traditional immune clonal selection algorithm(ICA), the proposed algorithms can enhance the diversity of the population, avoid the premature problems, and can accelerate convergence speed in some extent.

PID Control of Double-Loop Speed Control System Based on Bacteria-Particle Swarm Hybrid Optimization Algorithm

In order to improve the performance of dual-loop speed control system and overcome the defect of the traditional PID controller parameter tuning, a hybrid algorithm(BF-PSO) of Bacterial Foraging Optimization (BFO) algorithm and Particle Swarm Optimization(PSO) algorithm is proposed in this paper, and applied to PID parameter tuning of the double-loop speed control system. This algorithm combines the fast convergence characteristics of PSO and strong global search ability of BFO. Compared with the simulation results of the PSO and BFO, BF-PSO has better speed tracking performance, and higher convergence speed and robustness.

Satellite Attitude Tracking Controller Optimization Based on Particle Swarm Optimization

Parametric design of satellite tracking controller is an essential for almost modern satellites. To avoid selecting parameters by traditional experience, particle swarm optimization (PSO) algorithm is proposed to optimize the parameters of satellite attitude tracking controller, which is a radial basis function neural network based sliding mode controller. The mechanism of sharing information among particles is introduced to obtain solution. Numerical simulation results show that PSO can reach the optimal solution within 20 iterations. By updating the position and velocity of particles to seek solutions, PSO provides strong global search ability and convergent performance.

An Efficient Ant Colony System Based on Receding Horizon Control for the Aircraft Arrival Sequencing and Scheduling Problem

The aircraft arrival sequencing and scheduling (ASS) problem is a salient problem in air traffic control (ATC), which proves to be nondeterministic polynomial (NP) hard. This paper formulates the ASS problem in the form of a permutation problem and proposes a new solution framework that makes the first attempt at using an ant colony system (ACS) algorithm based on the receding horizon control (RHC) to solve it. The resultant RHC-improved ACS algorithm for the ASS problem (termed the RHC-ACS-ASS algorithm) is robust, effective, and efficient, not only due to that the ACS algorithm has a strong global search ability and has been proven to be suitable for these kinds of NP-hard problems but also due to that the RHC technique can divide the problem with receding time windows to reduce the computational burden and enhance the solution's quality. The RHC-ACS-ASS algorithm is extensively tested on the cases from the literatures and the cases randomly generated. Comprehensive investigations are also made for the evaluation of the influences of ACS and RHC parameters on the performance of the algorithm. Moreover, the proposed algorithm is further enhanced by using a two-opt exchange heuristic local search. Experimental results verify that the proposed RHC-ACS-ASS algorithm generally outperforms ordinary ACS without using the RHC technique and genetic algorithms (GAs) in solving the ASS problems and offers high robustness, effectiveness, and efficiency.

Application of improved PSO algorithm and wavelet analysis in foundation settlement prediction

The authors used the improved Particle Swarm Optimization(PSO) algorithm that has two subgroups and a mutation operator binding wavelet analysis to optimize neural network parameter to forecast the foundation settlement.Since the basic particle swarm optimization easily falls into the local minimal value,the authors divided the particle swarm into two subgroups.In one subgroup the inertia weight of the particle swarm optimization algorithm decreased when the iterations increased.And in the other subgroup the particle swarm optimization algorithm adopted big inertia weight to do the overall situation search.And the authors used this improved algorithm binding wavelet analysis to optimize the neural network parameter to forecast foundation settlement.The experimental result indicates that this method has strong global and local search ability,and has high forecast precision.

Replicating web contents using a hybrid particle swarm optimization

We consider the problem of placing copies of objects in a distributed web server system to minimize the cost of serving read and write requests when the web servers have limited storage capacities. We formulate the problem as a 0–1 optimization problem and present a hybrid particle swarm optimization algorithm to solve it. The proposed hybrid algorithm makes use of the strong global search ability of particle swarm optimization (PSO) and the strong local search ability of tabu search to obtain high quality solutions. The effectiveness of the proposed algorithm is demonstrated by comparing it with the genetic algorithm (GA), simple PSO, tabu search, and random placement algorithm on a variety of test cases. The simulation results indicate that the proposed hybrid approach outperforms the GA, simple PSO, and tabu search.

Using selection to improve quantum-behaved particle swarm optimisation

Quantum-behaved particle swarm optimisation (QPSO) algorithm is a global convergence guaranteed algorithms, which outperforms original PSO in search ability but has fewer parameters to control. This paper describes two selection mechanisms into QPSO to improve the search ability of QPSO. One is the QPSO with tournament selection (QPSO-TS) and the other is the QPSO with roulette-wheel selection (QPSO-RS). While the centre of position distribution of each particle in QPSO is determined by global best position and personal best position, in the QPSO with selection operation, the global best position is substituted by a candidate solution through selection. The QPSO with selection operation also maintains the mean best position of the swarm as in the previous QPSO to make the swarm more efficient in global search. The experiment results on benchmark functions show that both QPSO-RS and QPSO-TS have better performance and stronger global search ability than QPSO and standard PSO.

Hybrid artificial immune system and extremal optimization algorithm for permutation flowshop scheduling problem

The permutation flowshop scheduling problem (PFSP) is one of the most well-known and well-studied production scheduling problems with strong industrial background. This paper presents a new hybrid optimization algorithm which combines the strong global search ability of artificial immune system (AIS) with a strong local search ability of extremal optimization (EO) algorithm. The proposed algorithm is applied to a set of benchmark problems with a makespan criterion. Performance of the algorithm is evaluated. Comparison results indicate that this new method is an effective and competitive approach to the PFSP.

ECONOMIC LOAD DISPATCH BASED ON HYBRID EVOLUTIONARY ALGORITHM AND FUZZY NUMBER RANKING METHOD

In this paper we propose a new economic load dispatch model considered the cost function coefficients with uncertainties and the constraints of ramp rate. The uncertain parameters are represented by fuzzy numbers, with the model called fuzzy dynamic economic load dispatch model (FDELD). A novel hybrid evolutionary algorithm and fuzzy number ranking method is proposed to solve FDELD problem. Hybrid evolutionary algorithm combines evolutionary algorithm of very strong global search ability with quasi-simplex technique of better local search capability. The fuzzy number ranking method is used to compare the fuzzy cost function values when optimizing fuzzy cost function. In addition, this paper gives a novel method dealing with directly constrained conditions, and it is not necessary to construct penalty function, as a common disposal constraints method. The experimental study shows that FDELD is practical and the algorithm and techniques proposed are very efficient to solve FDELD problem.

Backcalculation Analysis of Pavement-Layer Moduli Using Genetic Algorithms

Most existing iterative backcalculation programs for pavement layer moduli arrive at their solutions by minimizing an objective function related to the differences between computed and measured surface deflections. Unfortunately, the solution surface of the backcalculation problem of pavement-layer moduli is known to contain many local minima. A potentially good backcalculation procedure would be one that has a strong global search ability to overcome the problem of local minima. The genetic algorithm (GA) is a technique that satisfies this requirement. The development of a backcalculation program known as NUS-GABACK using the genetic-algorithm approach is presented, along with the formulation and operations of the program. A detailed performance evaluation of the GA-based method is made against four other programs by solving five backcalculation problems with different structural composition. It was found that NUS-GABACK performed comparably well against the other programs and demonstrated consistency in the accuracies of backcalculated moduli.