scholarly journals A Fusion Crossover Mutation Sparrow Search Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Yanqiang Tang ◽  
Chenghai Li ◽  
Song Li ◽  
Bo Cao ◽  
Chen Chen
Keyword(s):  
Genetic Algorithm ◽  
Swarm Intelligence ◽  
Search Algorithm ◽  
Early Stage ◽  
Local Extremum ◽  
Flight Behavior ◽  
Local Optimum ◽  
Bird Swarm Algorithm ◽  
Crossover And Mutation ◽  
Swarm Intelligence Algorithm

Aiming at the inherent problems of swarm intelligence algorithm, such as falling into local extremum in early stage and low precision in later stage, this paper proposes an improved sparrow search algorithm (ISSA). Firstly, we introduce the idea of flight behavior in the bird swarm algorithm into SSA to keep the diversity of the population and reduce the probability of falling into local optimum; Secondly, we creatively introduce the idea of crossover and mutation in genetic algorithm into SSA to get better next-generation population. These two improvements not only keep the diversity of the population at all times but also make up for the defect that the sparrow search algorithm is easy to fall into local optimum at the end of the iteration. The optimization ability of the improved SSA is greatly improved.

Research of Image Registration Based on Maximum Entropy Template Selection Algorithm

2011 ◽  
Vol 268-270 ◽  
pp. 1138-1143
Author(s):  
Hong Ying Qin
Keyword(s):  
Genetic Algorithm ◽  
Image Registration ◽  
Maximum Entropy ◽  
Evolutionary Process ◽  
Adaptive Genetic Algorithm ◽  
Image Stitching ◽  
Local Optimum ◽  
Selection Algorithm ◽  
Template Selection ◽  
Crossover And Mutation

This paper concerns an improved adaptive genetic algorithm, and the method is applied to the Maximum Entropy Template Selection Algorithm image registration. This method includes adjusting the probability of crossover and mutation in the evolutionary process. The method can overcome the disadvantage of traditional genetic algorithm that is easy to get into a local optimum answer. Results show our method is insensitive to the ordering, rotation and scale of the input images so it can be used in image stitching and retrieval of images & videos.

scholarly journals A Reactive Population Approach on the Dolphin Echolocation Algorithm for Solving Cell Manufacturing Systems

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1389
Author(s):  
Ricardo Soto ◽  
Broderick Crawford ◽  
Rodrigo Olivares ◽  
César Carrasco ◽  
Eduardo Rodriguez-Tello ◽  
...  
Keyword(s):  
Swarm Intelligence ◽  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Optimization Problem ◽  
Local Optimum ◽  
Cell Manufacturing ◽  
Intelligence Algorithm ◽  
Autonomous Search ◽  
Parameter Values ◽  
Swarm Intelligence Algorithm

In this paper, we integrate the autonomous search paradigm on a swarm intelligence algorithm in order to incorporate the auto-adjust capability on parameter values during the run. We propose an independent procedure that begins to work when it detects a stagnation in a local optimum, and it can be applied to any population-based algorithms. For that, we employ the autonomous search technique which allows solvers to automatically re-configure its solving parameters for enhancing the process when poor performances are detected. This feature is dramatically crucial when swarm intelligence methods are developed and tested. Finding the best parameter values that generate the best results is known as an optimization problem itself. For that, we evaluate the behavior of the population size to autonomously be adapted and controlled during the solving time according to the requirements of the problem. The proposal is testing on the dolphin echolocation algorithm which is a recent swarm intelligence algorithm based on the dolphin feature to navigate underwater and identify prey. As an optimization problem to solve, we test a machine-part cell formation problem which is a widely used technique for improving production flexibility, efficiency, and cost reduction in the manufacturing industry decomposing a manufacturing plant in a set of clusters called cells. The goal is to design a cell layout in such a way that the need for moving parts from one cell to another is minimized. Using statistical non-parametric tests, we demonstrate that the proposed approach efficiently solves 160 well-known cell manufacturing instances outperforming the classic optimization algorithm as well as other approaches reported in the literature, while keeping excellent robustness levels.

scholarly journals Automatic Threshold Selections by exploration and exploitation of optimization algorithm in Record Deduplication

2016 ◽  
Vol 12 (11) ◽  
pp. 4515-4522
Author(s):  
K. Deepa ◽  
C. Vivek ◽  
S.Palanivel Rajan
Keyword(s):  
Genetic Algorithm ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Artificial Bee Colony ◽  
Similarity Function ◽  
Human Intervention ◽  
Exploration And Exploitation ◽  
Bee Colony ◽  
Threshold Setting ◽  
Swarm Intelligence Algorithm

A deduplication process uses similarity function to identify the two entries are duplicate or not by setting the threshold.  This threshold setting is an important issue to achieve more accuracy and it relies more on human intervention. Swarm Intelligence algorithm such as PSO and ABC have been used for automatic detection of threshold to find the duplicate records. Though the algorithms performed well there is still an insufficiency regarding the solution search equation, which is used to generate new candidate solutions based on the information of previous solutions.  The proposed work addressed two problems: first to find the optimal equation using Genetic Algorithm(GA) and next it adopts an modified  Artificial Bee Colony (ABC) to get the optimal threshold to detect the duplicate records more accurately and also it reduces human intervention. CORA dataset is considered to analyze the proposed algorithm.

One Optimizing Method for Moments of Inertia Applied with Improved Adaptive Genetic Algorithm

2011 ◽  
Vol 328-330 ◽  
pp. 54-57
Author(s):  
Heng Hui Sun ◽  
Min Zhou Luo ◽  
Xiang Dong ◽  
Shi Hong Zha
Keyword(s):  
Genetic Algorithm ◽  
Adaptive Genetic Algorithm ◽  
Local Optimum ◽  
Optimal Model ◽  
Moments Of Inertia ◽  
New Strategy ◽  
Optimizing Model ◽  
Crossover And Mutation ◽  
Standard Genetic Algorithm ◽  
Selection Of

In this paper, a new optimizing method for the moments of inertia of a mechanical structure was advanced. First, a new optimal model for the moments of inertia was advanced, which only involved with single objective and single variable, in order to reduce the calculating complexity of traditional multi-objective and multi-constrained optimizing model for the moments of inertia; Then, a new strategy for the probability selection of the crossover and mutation operation was improved to form the IAGA. The calculating results proved that, comparing to the Standard Genetic Algorithm (SGA), the IAGA improved in this paper had the advantage of converging faster, more powerfully searching, and less possible of falling into the local optimum. By that, the feasibility of the method advanced in this paper was demonstrated.

Application on Query of Distributed Database Based on Improved Genetic Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 4617-4621
Author(s):  
Fu Xing Chen ◽  
Xu Sheng Xie
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Search Algorithm ◽  
Random Search ◽  
Distributed Database ◽  
Improved Genetic Algorithm ◽  
Mutation Probability ◽  
Probabilistic Search ◽  
Crossover Probability ◽  
Crossover And Mutation

The query cost usually as an important criterion for a distributed database. The genetic algorithm is an adaptive probabilistic search algorithm, but the crossover and mutation probability usually keep a probability in traditional genetic algorithm. If the crossover probability keep a large value, the possibility of damage for genetic algorithm model is greater; In turn, if the crossover probability keep a small value, the search process will transform a slow processing or even stagnating. If the mutation probability keep a small value, a new individual can be reproduced difficultly; In turn, if the mutation probability keep a large value, the genetic algorithm will as a Pure random search algorithm. To solve this problem, proposed a improved genetic algorithm that multiple possibility of crossover and mutation based on k-means clustering algorithm. The experiment results indicate that the algorithm is effective.

scholarly journals A Hybrid Genetic Algorithm and Sperm Swarm Optimization (HGASSO) for Multimodal Functions

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Genetic Algorithm ◽  
Convergence Rates ◽  
Hybrid Genetic Algorithm ◽  
Main Idea ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Quality Of Results ◽  
Metaheuristic Methods ◽  
Crossover And Mutation

In this paper, we propose a hybrid algorithm combining two different metaheuristic methods, “Genetic Algorithms (GA)” and “Sperm Swarm Optimization (SSO)”, for the global optimization of multimodal benchmarks functions. The proposed Hybrid Genetic Algorithm and Sperm Swarm Optimization (HGASSO) operates based on incorporates concepts from GA and SSO in which generates individuals in a new iteration not only by crossover and mutation operations as proposed in GA, but also by techniques of local search of SSO. The main idea behind this hybridization is to reduce the probability of trapping in local optimum of multi modal problem. Our algorithm is compared against GA, and SSO metaheuristic optimization algorithms. The experimental results using a suite of multimodal benchmarks functions taken from the literature have evinced the superiority of the proposed HGASSO approach over the other approaches in terms of quality of results and convergence rates in which obtained good results in solving the multimodal benchmarks functions that include cosine, sine, and exponent in their formulation.

scholarly journals ENHANCED ALGORITHM FOR REDUCTION OF ACTIVE POWER LOSS

2018 ◽  
Vol 6 (7) ◽  
pp. 45-51
Author(s):  
K. Lenin
Keyword(s):  
Genetic Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Search Algorithm ◽  
Stochastic Search ◽  
Active Power ◽  
Systems Simulation ◽  
Crossover And Mutation ◽  
Power Problem ◽  
Growth And Reproduction

In this paper, Enhanced Aggressive Weed Optimization (EWO) algorithm is applied to solve the optimal reactive power Problem. Aggressive Weed Optimization is a stochastic search algorithm that imitate natural deeds of weeds in colonize and detection of appropriate place for growth and reproduction. Enhanced Aggressive Weed Optimization (EWO) algorithm is based on hybridization of genetic algorithm with weed optimization algorithm which refers combination of crossover and mutation of genetic algorithm, and by the use of the cross factor new species are arisen. Proposed Enhanced Aggressive Weed Optimization (EWO) algorithm has been evaluated in standard IEEE 118 & practical 191 bus test systems. Simulation results show   that our projected approach outperforms all the entitled reported algorithms in minimization of real power loss and voltage profiles are within the specified limits.

A New Flatness Evaluation-Rotation Method Based on GA

2010 ◽  
Vol 139-141 ◽  
pp. 2033-2037 ◽  
Author(s):  
Yan Ming Jiang ◽  
Gui Xiong Liu
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Three Dimensions ◽  
Local Optimum ◽  
Mutation Probability ◽  
Projection Length ◽  
Rotation Method ◽  
Minimum Zone ◽  
Crossover And Mutation

Flatness is one fundamental element of geometric forms, and the flatness evaluation is particularly important for ensuring the quality of industrial products. This paper presents a new flatness evaluation in the view of the minimum zone evaluation - rotation method based on genetic algorithm. This method determines the minimum zone through rotating measurement points in three dimensions coordinate. The points are firstly rotated about coordinate axes. Then they are projected in one axis, and the smallest projection length is the flatness value. The rotation angles are optimized by genetic algorithm to improve search efficiency. An exponential fitness function and the rotation angles range is designed on the basis of flatness characteristics. An adaptive mode of crossover and mutation probability is used to avoid local optimum. The results show this method can search the minimum zone and converge rapidly.

Optimization test of a rule-based swarm intelligence simulation for the conceptual design process

2020 ◽  
Vol 34 (4) ◽  
pp. 477-491
Author(s):  
Asli Agirbas
Keyword(s):  
Genetic Algorithm ◽  
Swarm Intelligence ◽  
Case Studies ◽  
Surface Area ◽  
Conceptual Design ◽  
Design Process ◽  
Design Problems ◽  
Rule Based ◽  
Building Height ◽  
Swarm Intelligence Algorithm

AbstractToday, in the field of architecture, bio-inspired algorithms can be used to design and seek solutions to design problems. Two of the most popular algorithms are the genetic algorithm (GA) and swarm intelligence algorithm. However, no study has examined the simultaneous use of these two bio-inspired algorithms in the field of architecture. Therefore, this study aims to test whether these two bio-inspired algorithms can work together. To this end, GA is used in this study to optimize the rule-based swarm algorithm for the conceptual design process. In this optimization test, the objective was to increase the surface area, and the constraints are parcel boundary and building height. Further, the forms associated with swarm agents were determined as variables. Following the case studies, the study concludes that the two bio-inspired algorithms can effectively work together.

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