scholarly journals Quasi-Affine Transformation Evolutionary with Double Excellent Guidance

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Tien-Wen Sung ◽  
Baohua Zhao ◽  
Xin Zhang
Keyword(s):  
Population Structure ◽  
Affine Transformation ◽  
Optimal Solution ◽  
Collaborative Optimization ◽  
Optimal Position ◽  
Mutation Strategy ◽  
Local Optimal Solution ◽  
Global Optimal ◽  
The Individual ◽  
Double Population

The Quasi-Affine Transformation Evolutionary (QUATRE) algorithm is a swarm-based collaborative optimization algorithm, which has drawn attention from researchers due to its simple structure, easy implementation, and powerful performance. However, it needs to be improved regarding the exploration, especially in the late stage of evolution, and the problem of easy falling into a local optimal solution. This paper proposes an improved algorithm named Quasi-Affine Transformation Evolutionary with double excellent guidance (QUATRE-DEG). The algorithm uses not only the global optimal solution but also the global suboptimal solution to guide the individual evolution. We establish a model to determine the guiding force by the distance between the global optimal position and the suboptimal position and propose a new mutation strategy through the double population structure. The optimization of population structure and the improvement of operation mechanisms bring more exploration for the algorithm. To optimize the algorithm, the experiments on parameter settings were made to determine the size of the subpopulation and to achieve a balance between exploration and development. The performance of QUATRE-DEG algorithm is evaluated under CEC2013 and CEC2014 test suites. Through comparison and analysis with some ABC variants known for their strong exploration ability and advanced QUATRE variants, the competitiveness of the proposed QUATRE-DEG algorithm is validated.

AO-BBO: A Novel Optimization Algorithm and Its Application in Plant Drug Extraction

2019 ◽  
Vol 19 (2) ◽  
pp. 139-145 ◽  
Author(s):  
Bote Lv ◽  
Juan Chen ◽  
Boyan Liu ◽  
Cuiying Dong
Keyword(s):  
Optimization Algorithm ◽  
Learning Strategy ◽  
Optimal Solution ◽  
Population Diversity ◽  
Global Optimal Solution ◽  
Plant Medicine ◽  
Suitability Index ◽  
Sensing Model ◽  
Local Optimal Solution ◽  
Global Optimal

<P>Introduction: It is well-known that the biogeography-based optimization (BBO) algorithm lacks searching power in some circumstances. </P><P> Material & Methods: In order to address this issue, an adaptive opposition-based biogeography-based optimization algorithm (AO-BBO) is proposed. Based on the BBO algorithm and opposite learning strategy, this algorithm chooses different opposite learning probabilities for each individual according to the habitat suitability index (HSI), so as to avoid elite individuals from returning to local optimal solution. Meanwhile, the proposed method is tested in 9 benchmark functions respectively. </P><P> Result: The results show that the improved AO-BBO algorithm can improve the population diversity better and enhance the search ability of the global optimal solution. The global exploration capability, convergence rate and convergence accuracy have been significantly improved. Eventually, the algorithm is applied to the parameter optimization of soft-sensing model in plant medicine extraction rate. Conclusion: The simulation results show that the model obtained by this method has higher prediction accuracy and generalization ability.</P>

Optimization Method Based on a Mix Strategy

2013 ◽  
Vol 816-817 ◽  
pp. 1154-1157
Author(s):  
Xu Yin ◽  
Ai Min Ji
Keyword(s):  
Optimization Problems ◽  
Optimal Solution ◽  
Optimization Method ◽  
Local Solution ◽  
Collaborative Optimization ◽  
Design Variables ◽  
Gradient Optimization ◽  
Local Optimal Solution ◽  
Optimal Values ◽  
Low Efficiency

To solve problems that exist in optimal design such as falling into local optimal solution easily and low efficiency in collaborative optimization, a new mix strategy optimization method combined design of experiments (DOE) with gradient optimization (GO) was proposed. In order to reduce the effect on the result of optimization made by the designers decision, DOE for preliminary analysis of the function model was used, and the optimal values obtained in DOE stage was taken as the initial values of design variables in GO stage in the new optimization method. The reducer MDO problem was taken as a example to confirm the global degree, efficiency, and accuracy of the method. The results show the optimization method could not only avoid falling into local solution, but also have an obvious superiority in treating the complex collaborative optimization problems.

The Research on Modified K-Means Algorithm Based on GA&SA

2013 ◽  
Vol 347-350 ◽  
pp. 3242-3246
Author(s):  
Zhe Feng Zhu ◽  
Xiao Bin Hui ◽  
Yi Qian Cao ◽  
Wan Xiang Lian
Keyword(s):  
Local Search ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Global Search ◽  
Local Optimization ◽  
Effective Algorithm ◽  
Global Optimal Solution ◽  
Local Optimal Solution ◽  
Global Optimal ◽  
Clustering Center

The traditional K-means clustering algorithm has the disadvantage of weakness in overall search, easily falling into local optimization, highly reliance on initial clustering center. Aiming at the drawback of falling into partial optimization, putting forward a modified K-means algorithm mixing GA and SA, which combined the advantages of global search ability of GA and local search, to avoid K-means algorithm to lost into local optimal solution. The results of simulation show that the performance of above-mentioned algorithm is better in the optimization capacity than before, and easier to get the global optimal solution. It is an effective algorithm.

scholarly journals Multipopulation Genetic Algorithm Based on GPU for Solving TSP Problem

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Boqun Wang ◽  
Hailong Zhang ◽  
Jun Nie ◽  
Jie Wang ◽  
Xinchen Ye ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Coarse Grained ◽  
Mutation Rates ◽  
Adaptive Mutation ◽  
Original Population ◽  
Elite Strategy ◽  
Mutation Strategy ◽  
Local Optimal Solution ◽  
Crossover Method

A GPU-based Multigroup Genetic Algorithm was proposed, which parallelized the traditional genetic algorithm with a coarse-grained architecture island model. The original population is divided into several subpopulations to simulate different living environments, thus increasing species richness. For each subpopulation, different mutation rates were adopted, and the crossover results were optimized by combining the crossover method based on distance. The adaptive mutation strategy based on the number of generations was adopted to prevent the algorithm from falling into the local optimal solution. An elite strategy was adopted for outstanding individuals to retain their superior genes. The algorithm was implemented with CUDA/C, combined with the powerful parallel computing capabilities of GPUs, which greatly improved the computing efficiency. It provided a new solution to the TSP problem.

scholarly journals AGV optimal path planning based on improved ant colony algorithm

2018 ◽  
Vol 232 ◽  
pp. 03052 ◽  
Author(s):  
Chengwei He ◽  
Jian Mao
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Optimal Solution ◽  
Ant Colony ◽  
Undirected Network ◽  
Optimal Path Planning ◽  
Local Optimal Solution ◽  
Global Optimal ◽  
Improved Ant Colony Algorithm

Using the traditional Ant Colony Algorithm for AGV path planning is easy to fall into the local optimal solution and lacking the capability of obstacle avoidance in the complex storage environment. In this paper, by constructing the MAKLINK undirected network routes and the heuristic function is optimized in the Ant Colony Algorithm, then the AGV path reaches the global optimal path and has the ability to avoid obstacles. According to research, the improved Ant Colony Algorithm proposed in this paper is superior to the traditional Ant Colony Algorithm in terms of convergence speed and the distance of optimal path planning.

A Novel Global ABC Algorithm with Self-Perturbing

2017 ◽  
Vol 26 (4) ◽  
pp. 729-740 ◽  
Author(s):  
Shuliang Zhou ◽  
Dongqing Feng ◽  
Panpan Ding
Keyword(s):  
Optimal Solution ◽  
Population Based ◽  
Natural Phenomenon ◽  
Local Optimum ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Mutation Strategy ◽  
Engineering Problems ◽  
Novel Method ◽  
Global Optimal

AbstractArtificial bee colony (ABC) is a kind of a metaheuristic population-based algorithms proposed in 2005. Due to its simple parameters and flexibility, the ABC algorithm is applied to engineering problems, algebra problems, and so on. However, its premature convergence and slow convergence speed are inherent shortcomings. Aiming at the shortcomings, a novel global ABC algorithm with self-perturbing (IGABC) is proposed in this paper. On the basis of the original search equation, IGABC adopts a novel self-adaptive search equation, introducing the guidance of the global optimal solution. The search method improves the convergence precision and the global search capacity. An excellent leader can lead the whole team to obtain more success. In order to obtain a better “leader,” IGABC proposes a novel method with global self-perturbing. To avoid falling into the local optimum, this paper designed a new mutation strategy that simulates the natural phenomenon of sick fish being eaten.

Chaotic Mutation Bee Evolution Algorithm

2012 ◽  
Vol 482-484 ◽  
pp. 1636-1639
Author(s):  
Yuan Yao ◽  
Yan Ling Zou ◽  
Qi Man Wu ◽  
Zhong Ren Guan
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Fast Convergence ◽  
Evolution Process ◽  
Global Optimal Solution ◽  
Iterative Calculation ◽  
Global Optimal ◽  
Evolution Algorithm ◽  
The Individual

In order to make full use of chaotic mutation genetic algorithm and the chaotic mutation and bee evolution algorithm, the characteristics of the two algorithms, and the combination of chaotic mutation bee evolution algorithm is proposed. The algorithm in bee evolution process, to adapt to the value of group of smaller portions of the variation of individuals to chaos; to adapt to the value of group of large part of the individual, to the best individual as the center, change crossover operation, each generation is the best individual immune evolutionary iterative calculation. Thus, as the iteration, the algorithm not only fast convergence, and can also by a higher accuracy by the global optimal solution.

Modeling of Well Service and Workover to Optimize Scheduling of Oil Well Maintenance

2016 ◽  
Vol 836 ◽  
pp. 311-316 ◽  
Author(s):  
Sudiyono Kromodihardjo ◽  
Ergo Swasono Kromodihardjo
Keyword(s):  
Optimal Solution ◽  
Simulation Software ◽  
High Rate ◽  
Oil Well ◽  
Well Production ◽  
Breakdown Time ◽  
Well Location ◽  
Local Optimal Solution ◽  
Global Optimal ◽  
Oil Company

Well maintenance (well service and workover) is an operation needed by oil company to guarantee the optimum productionof its oil well.Well maintenance is performed using large equipment called hydraulic workover unit (HWU-Rig) which is available in limited number. Scheduling sequence of the HWU-Rig to do well service must meet the goal of the maintenance that is to minimize the loss of oil well production due to well breakdown. Thus minimizing breakdown time of well with high rate production is a priority. However, scheduling secuence of the HWU-Rig to perform its task for few days ahead become complicated due to the numerous alternatives of secuence to choose. Each alternatives of sequence yields a certain production loss. Arbitrarily scheduling sequence may not yield the goal og minimizing the loss of well production. This research was done by analyzing workover scheduling system and data from Kondur Petroleum such as well location, well production rate, and service time needed to be performed on wells. Algorithm to create schedulling sequence was developed in the research. The algorithm was then implemented in discrete simulation software, and yield the result of absolute global optimal solution, near optimal solution and local optimal solution of the HWU scheduling problem.

scholarly journals Rate Maximization for Suspicious Multicast Communication Networks with Full-Duplex Proactive Monitoring

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Dongsheng Liu ◽  
Sai Zhao ◽  
Quanzhong Li ◽  
Jiayin Qin
Keyword(s):  
Communication Network ◽  
Communication Networks ◽  
Optimal Solution ◽  
Full Duplex ◽  
Multicast Communication ◽  
Multicast Networks ◽  
Rate Maximization ◽  
Local Optimal Solution ◽  
Global Optimal ◽  
Proactive Monitoring

In this paper, we investigate the optimization of the monitoring rate for a suspicious multicast communication network with a legitimate full-duplex (FD) monitor, where the FD monitor is proactive to jam suspicious receivers and eavesdrop from the suspicious transmitter simultaneously. To effectively monitor the suspicious communication over multicast networks, we maximize the monitoring rate under the outage probability constraint of the suspicious multicast communication network and the jamming power constraint at the FD monitor. The formulated optimization problem is nonconvex, and its global optimal solution is hard to obtain. Thus, we propose a constrained concave convex procedure- (CCCP-) based iterative algorithm, which is able to achieve a local optimal solution. Simulation results demonstrate that the proposed proactive eavesdropping scheme with optimal jamming power outperforms the conventional passive eavesdropping scheme.

scholarly journals Parallel Compact Differential Evolution for Optimization Applied to Image Segmentation

2020 ◽  
Vol 10 (6) ◽  
pp. 2195
Author(s):  
Xiao Sui ◽  
Shu-Chuan Chu ◽  
Jeng-Shyang Pan ◽  
Hao Luo
Keyword(s):  
Image Segmentation ◽  
Differential Evolution ◽  
Communication Strategies ◽  
Optimal Solution ◽  
Mean Value ◽  
Computing Power ◽  
Elite Strategy ◽  
Local Optimal Solution ◽  
The Individual

A parallel compact Differential Evolution (pcDE) algorithm is proposed in this paper. The population is separated into multiple groups and the individual is run by using the method of compact Differential Evolution. The communication is implemented after predefined iterations. Two communication strategies are proposed in this paper. The first one is to replace the local optimal solution by global optimal solution in all groups, which is called optimal elite strategy (oe); the second one is to replace the local optimal solution by mean value of the local optimal solution in all groups, which is called mean elite strategy (me). Considering that the pcDE algorithm does not need to store a large number of solutions, the algorithm can adapt to the environment with weak computing power. In order to prove the feasibility of pcDE, several groups of comparative experiments are carried out. Simulation results based on the 25 test functions demonstrate the efficacy of the proposed two communication strategies for the pcDE. Finally, the proposed pcDE is applied to image segmentation and experimental results also demonstrate the superior quality of the pcDE compared with some existing methods.

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