Hurricane Search algorithm a new model for function optimization

2014 ◽  
Author(s):  
Ismail Rbouh ◽  
Abdelhakim Ameur El Imrani
Keyword(s):  
Search Algorithm ◽  
Function Optimization ◽  
New Model

Performances of Adaptive Cuckoo Search Algorithm in Engineering Optimization

2016 ◽  
pp. 676-699 ◽  
Author(s):  
Pauline Ong ◽  
S. Kohshelan
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Breeding Strategy ◽  
Function Optimization ◽  
Engineering Optimization ◽  
Optimal Solutions ◽  
Step Size ◽  
Swarm Optimization ◽  
Searching Strategy

A new optimization algorithm, specifically, the cuckoo search algorithm (CSA), which inspired by the unique breeding strategy of cuckoos, has been developed recently. Preliminary studies demonstrated the comparative performances of the CSA as opposed to genetic algorithm and particle swarm optimization, however, with the competitive advantage of employing fewer control parameters. Given enough computation, the CSA is guaranteed to converge to the optimal solutions, albeit the search process associated to the random-walk behavior might be time-consuming. Moreover, the drawback from the fixed step size searching strategy in the inner computation of CSA still remain unsolved. The adaptive cuckoo search algorithm (ACSA), with the effort in the aspect of integrating an adaptive search strategy, was attached in this study. Its beneficial potential are analyzed in the benchmark test function optimization, as well as engineering optimization problem. Results showed that the proposed ACSA improved over the classical CSA.

Hybrid improved slime mould algorithm with adaptive β hill climbing for numerical optimization

2021 ◽  
Vol 40 (1) ◽  
pp. 1667-1679
Author(s):  
Kangjian Sun ◽  
Heming Jia ◽  
Yao Li ◽  
Zichao Jiang
Keyword(s):  
Search Algorithm ◽  
Computation Time ◽  
Function Optimization ◽  
Hill Climbing ◽  
Multilayer Perceptrons ◽  
Design Problems ◽  
Slime Mould ◽  
Wilcoxon Rank Sum Test ◽  
Comparison Results ◽  
Engineering Design Problems

Slime mould algorithm (SMA) is a novel metaheuristic that simulates foraging behavior of slime mould. Regarding its drawbacks and properties, a hybrid optimization (BTβSMA) based on improved SMA is proposed to produce the higher-quality optimal results. Brownian motion and tournament selection mechanism are introduced into the basic SMA to improve the exploration capability. Moreover, a local search algorithm (Adaptive β-hill climbing, AβHC) is hybridized with the improved SMA. It is considered from boosting the exploitation trend. The proposed BTβSMA algorithm is evaluated in two main phases. Firstly, the two improved hybrid variants (BTβSMA-1 and BTβSMA-2) are compared with the basic SMA algorithm through 16 benchmark functions. Also, the performance of winner is further evaluated through comparisons with 7 state-of-the-art algorithms. The simulation results report fitness and computation time. The convergence curve and boxplot visualize the effects of fitness. The comparison results on the function optimization suggest that BTβSMA is superior to competitors. Wilcoxon rank-sum test is also employed to investigate the significance of the results. Secondly, the applicability on real-world tasks is proved by solving structure engineering design problems and training multilayer perceptrons. The numerical results indicate the merits of the BTβSMA algorithm in terms of solution precision.

An improved differential harmony search algorithm for function optimization problems

2018 ◽  
Vol 23 (13) ◽  
pp. 4827-4852 ◽  
Author(s):  
Lin Wang ◽  
Huanling Hu ◽  
Rui Liu ◽  
Xiaojian Zhou
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Function Optimization

Incorporating Allee effects in fish stock–recruitment models and applications for determining reference points

2002 ◽  
Vol 59 (2) ◽  
pp. 242-249 ◽  
Author(s):  
D G Chen ◽  
J R Irvine ◽  
A J Cass
Keyword(s):  
Search Algorithm ◽  
Information Matrix ◽  
Natural Extension ◽  
Reference Points ◽  
Fish Stock ◽  
Model Parameters ◽  
Allee Effects ◽  
Probability Curve ◽  
New Model ◽  
Stock Recruitment

A new type of stock–recruitment model is examined that incorporates Allee effects, which may occur when fish populations are small. The model is a natural extension of traditional models, which only incorporate the negative effects of increasing density on fecundity and (or) survival. Because the new model is intrinsically nonlinear and because of convergence problems at local optima, we use a maximum likelihood approach with a global genetic search algorithm to estimate model parameters. Parameter uncertainty is obtained from the inverse of the Fisher information matrix. Based on this new model, an extinction probability curve is developed using the parameter defining the Allee effects. This curve can readily be used to calculate the theoretical probability of extinction for a single brood line in one generation for any particular spawner number or biomass. Alternatively, because managers may wish to assign reference points corresponding to particular extinction probabilities, spawner numbers can be determined for these reference points. Two Pacific salmon populations, North Thompson coho (Oncorhynchus kisutch) and Chilko sockeye (O. nerka), are used to demonstrate the approach. It is found that the Allee effect parameter is statistically significant for the Thompson coho, but not for Chilko sockeye.

scholarly journals An Improved Squirrel Search Algorithm for Global Function Optimization

Algorithms ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 80 ◽  
Author(s):  
Yanjiao Wang ◽  
Tianlin Du
Keyword(s):  
Search Algorithm ◽  
Statistical Tests ◽  
Search Space ◽  
Search Method ◽  
Population Diversity ◽  
Convergence Speed ◽  
Function Optimization ◽  
Experimental Results ◽  
Selection Strategy ◽  
Evolutionary Information

An improved squirrel search algorithm (ISSA) is proposed in this paper. The proposed algorithm contains two searching methods, one is the jumping search method, and the other is the progressive search method. The practical method used in the evolutionary process is selected automatically through the linear regression selection strategy, which enhances the robustness of squirrel search algorithm (SSA). For the jumping search method, the ‘escape’ operation develops the search space sufficiently and the ‘death’ operation further explores the developed space, which balances the development and exploration ability of SSA. Concerning the progressive search method, the mutation operation fully preserves the current evolutionary information and pays more attention to maintain the population diversity. Twenty-one benchmark functions are selected to test the performance of ISSA. The experimental results show that the proposed algorithm can improve the convergence accuracy, accelerate the convergence speed as well as maintain the population diversity. The statistical test proves that ISSA has significant advantages compared with SSA. Furthermore, compared with five other intelligence evolutionary algorithms, the experimental results and statistical tests also show that ISSA has obvious advantages on convergence accuracy, convergence speed and robustness.

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