Artificial Glowworm Swarm Optimization Research Articles

Modeling of Nonlinear Load Electric Energy Measurement and Evaluation System Based on Artificial Intelligence Algorithm

Background: To improve the modeling efficiency of nonlinear load electric energy metering evaluation system, a method based on artificial intelligence algorithm was proposed. Methods: First introduces the artificial glowworm swarm optimization extreme learning machine, a powerful ability of global optimization using artificial firefly algorithm. Then, find the hours of training error, extreme learning machine model, input weighting matrix, and hidden layer offset matrix. Moreover, there is a certain period in a given area in our country, power load simulation through the experiment, verifying the validity and superiority of the model. Results: The experimental results show that the traditional BP neural network has the largest prediction relative error, and the stability of BP neural network is poor, and the relative error time is large, which is related to the defect of the neural network itself. The prediction effect of SVM method is better than that of BP neural network, because SVM has a strict theoretical and mathematical basis, so its generalization ability is better than that of BP neural network, and the algorithm has global optimality. Conclusion: As can be seen from the chart analysis, GSO-ELM algorithm performs better than both in terms of stability and test error. It is proved that the modeling of nonlinear load electrical energy measurement and evaluation system based on artificial intelligence algorithm is superior and effective. The proposed algorithm outperforms very well over the existing literature.

Improved Wolf Pack Algorithm for UAV Path Planning Problem

In order to obtain the global optimal path of UAV flight path planning in low altitude penetration mode, this paper studies a method of UAV 3d flight path planning. Aiming at the shortcomings of original wolf pack algorithm, such as poor local search ability, invariable step size and slow convergence rate, this paper proposes an improved wolf pack algorithm based on the fluorescein guide mechanism in the artificial glowworm swarm optimization algorithm, which also improves the searching mode in wandering behavior, update the individual position in wolf pack with the teaching and learning optimization algorithms with feedback mechanism. In addition, dynamic mutation strategy was introduced to jump out of the local optimal solution. The results show that the improved algorithm is more effective in solving the problem of UAV 3d trajectory planning.

Glowworm Swarm Optimization Algorithm for Solving Double Numerical Integration

A new method for solving double numerical Integration based on artificial glowworm swarm optimization algorithm (GSO) is presented. GSO is used to optimize the node points on the both direction range in rectangular Integration domain to get a more precise Integration result. Simulation examples of Integration show the algorithm is a validated method with high precision and powerful self-adapting. The algorithm has value in numerical calculation and engineering practice.

Artificial glowworm swarm optimization algorithm based on adaptive t distribution mixed mutation

The convergence speed of Artificial Glowworm Swarm Optimization(AGSO) algorithm declines,even falls into local minimums,when some glowworms gather in non whole extreme points or some glowworms wander around aimlessly.Concerning this problem,an AGSO algorithm based on adaptive t distribution mixed mutation was proposed.Adaptive t distribution mutation and optimization adjustment mutation was introduced into the AGSO algorithm to improve the diversity of glowworm swarm,and prevent the AGSO algorithm from falling into local minimums.Mutation control factor was defined.Combining history status information,the description of adaptive t distribution mixed mutation was given.The mutation method could enhance ability of global exploration and local development.The emulation results of representative test functions and many application examples show that the proposed algorithm is reliable and efficient.Meanwhile,this algorithm is better than tradition algorithm in terms of speed and precision for seeking the optimum.

Artificial Glowworm Swarm Optimization Algorithm for Solving Multi-Objective Constrained Optimization

Focused on the disadvantages of some current constrained optimization algorithm, glowworm swarm multi-objective optimization algorithm ( GSMOA） is proposed in this paper. The main character of this algorithm conforms to feasibility rules and adapts self- adaptive penalty function to search feasible solutions. This algorithm has been tested on 4 standard functions and it shows that the proposed algorithm has more advantage in the convergence rate and the solution precision.

Hybrid Artificial Glowworm Swarm Optimization Algorithm for Solving Constrained Engineering Problem

In this paper, based on glowworm swarm (GS) and artificial fish swarm (AFS) with differential evolution (DE) optimization algorithm, a new hybrid artificial glowworm swarm optimization (HGSO) algorithm is proposed. We use HGSO to solve engineering optimization design problem. The results show that the HGSO has faster convergence, higher precision and is more effective for solving constrained engineering optimization problem.

Hybrid Artificial Glowworm Swarm Optimization Algorithm for Solving Multi-dimensional Knapsack Problem

In this paper, a hybrid artificially glowworm swarm optimization algorithm to solve multidimensional 0-1 knapsack problem is proposed. The algorithm utilizes two important strategies, how to select the item based on its unit volume value and the binary glowworm swarm optimization algorithm, 37 multidimensional 0-1 knapsack test instances are tested by the produced algorithm, The integrated performance of the produced algorithm is rather satisfied, and the hybrid glowworm swarm optimization algorithm is rather efficient for solving multidimensional 0-1 knapsack problem.

Artificial Glowworm Swarm Optimization Algorithm for Solving 0-1 Knapsack Problem

In this paper, an artificial glowworm swarm optimization algorithm for solving 0-1 knapsack problem is proposed, and the detailed realization of the algorithm is illustrated. According to intelligent algorithm for knapsack problem, the question of sensitive parameter’s choice is avoided under the greed idea. Simulation results show that the artificial glowworm swarm optimization algorithm for solving 0-1 knapsack problems is feasible and effective.