Journal ArticleDOI
A novel collaborative optimization algorithm in solving complex optimization problems
Wu Deng,Huimin Zhao,Huimin Zhao,Li Zou,Li Zou,Li Zou,Guangyu Li,Guangyu Li,Xinhua Yang,Daqing Wu,Daqing Wu +10 more
- Vol. 21, Iss: 15, pp 4387-4398
Reads0
Chats0
TLDR
The experiment results show that the proposed MGACACO algorithm can avoid falling into the local extremum, and takes on better search precision and faster convergence speed.Abstract:
To overcome the deficiencies of weak local search ability in genetic algorithms (GA) and slow global convergence speed in ant colony optimization (ACO) algorithm in solving complex optimization problems, the chaotic optimization method, multi-population collaborative strategy and adaptive control parameters are introduced into the GA and ACO algorithm to propose a genetic and ant colony adaptive collaborative optimization (MGACACO) algorithm for solving complex optimization problems. The proposed MGACACO algorithm makes use of the exploration capability of GA and stochastic capability of ACO algorithm. In the proposed MGACACO algorithm, the multi-population strategy is used to realize the information exchange and cooperation among the various populations. The chaotic optimization method is used to overcome long search time, avoid falling into the local extremum and improve the search accuracy. The adaptive control parameters is used to make relatively uniform pheromone distribution, effectively solve the contradiction between expanding search and finding optimal solution. The collaborative strategy is used to dynamically balance the global ability and local search ability, and improve the convergence speed. Finally, various scale TSP are selected to verify the effectiveness of the proposed MGACACO algorithm. The experiment results show that the proposed MGACACO algorithm can avoid falling into the local extremum, and takes on better search precision and faster convergence speed.read more
Citations
More filters
Journal ArticleDOI
An Improved Ant Colony Optimization Algorithm Based on Hybrid Strategies for Scheduling Problem
Wu Deng,Junjie Xu,Huimin Zhao +2 more
TL;DR: The experiment results show that the proposed ICMPACO algorithm can effectively obtain the best optimization value in solving TSP and effectively solve the gate assignment problem, obtain better assignment result, and it takes on better optimization ability and stability.
Journal ArticleDOI
A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm
TL;DR: The fuzzy information entropy can accurately and more completely extract the characteristics of the vibration signal, the improved PSO algorithm can effectively improve the classification accuracy of LS-SVM, and the proposed fault diagnosis method outperforms the other mentioned methods.
Journal ArticleDOI
An Improved Quantum-Inspired Differential Evolution Algorithm for Deep Belief Network
TL;DR: An improved quantum-inspired differential evolution (MSIQDE), namely MSIQDE algorithm based on making use of the merits of the Mexh wavelet function, standard normal distribution, adaptive quantum state update, and quantum nongate mutation, is proposed to avoid premature convergence and improve the global search ability.
Journal ArticleDOI
Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients.
TL;DR: A new efficient diagnostic approach to integrate machine learning and gas chromatography-mass spectrometry (GC-MS), named GEE, is proposed to identify the paraquat poisoned patients and might serve as a novel candidate diagnosis of PQ-poisoned patients.
Journal ArticleDOI
Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies
Hao Chen,Ali Asghar Heidari,Ali Asghar Heidari,Huiling Chen,Mingjing Wang,Zhifang Pan,Amir H. Gandomi +6 more
TL;DR: The first powerful variant of the Harris hawks optimization (HHO) integrates chaos strategy, topological multi-population strategy, and differential evolution (DE) strategy and is compared with a range of other methods.
References
More filters
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Journal ArticleDOI
Ant colonies for the travelling salesman problem
TL;DR: An artificial ant colony capable of solving the travelling salesman problem (TSP) is described, an example of the successful use of a natural metaphor to design an optimization algorithm.
Journal ArticleDOI
Hybrid metaheuristics in combinatorial optimization: A survey
TL;DR: A survey of some of the most important lines of hybridization of metaheuristics with other techniques for optimization, which includes, for example, the combination of exact algorithms and meta heuristics.
Journal ArticleDOI
The biological principles of swarm intelligence
TL;DR: The underlying mechanisms of complex collective behaviors of social insects, from the concept of stigmergy to the theory of self-organization in biological systems, are described and four functions that emerge at the level of the colony and that organize its global behavior are proposed.
Journal ArticleDOI
A rapid learning algorithm for vehicle classification
TL;DR: Experimental results demonstrate that the proposed approaches not only speed up the training and incremental learning processes of AdaBoost, but also yield better or competitive vehicle classification accuracies compared with several state-of-the-art methods, showing their potential for real-time applications.
Related Papers (5)
An Improved Ant Colony Optimization Algorithm Based on Hybrid Strategies for Scheduling Problem
Wu Deng,Junjie Xu,Huimin Zhao +2 more