scholarly journals A novel animal migration algorithm for global numerical optimization

2016 ◽  
Vol 13 (1) ◽  
pp. 259-285 ◽  
Author(s):  
Qifang Luo ◽  
Mingzhi Ma ◽  
Yongquan Zhou
Keyword(s):  
Numerical Optimization ◽  
Cuckoo Search ◽  
Test Functions ◽  
Migration Process ◽  
Global Numerical Optimization ◽  
Bee Colony ◽  
Intelligent Optimization Algorithms ◽  
Animal Migration Optimization ◽  
Animal Migration ◽  
Benchmark Test Functions

Animal migration optimization (AMO) searches optimization solutions by migration process and updating process. In this paper, a novel migration process has been proposed to improve the exploration and exploitation ability of the animal migration optimization. Twenty-three typical benchmark test functions are applied to verify the effects of these improvements. The results show that the improved algorithm has faster convergence speed and higher convergence precision than the original animal migration optimization and other some intelligent optimization algorithms such as particle swarm optimization (PSO), cuckoo search (CS), firefly algorithm (FA), bat-inspired algorithm (BA) and artificial bee colony (ABC).

A ranking-based adaptive artificial bee colony algorithm for global numerical optimization

2017 ◽  
Vol 417 ◽  
pp. 169-185 ◽  
Author(s):  
Laizhong Cui ◽  
Genghui Li ◽  
Xizhao Wang ◽  
Qiuzhen Lin ◽  
Jianyong Chen ◽  
...  
Keyword(s):  
Numerical Optimization ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Global Numerical Optimization ◽  
Bee Colony

Hybridizing cuckoo search algorithm with bat algorithm for global numerical optimization

2018 ◽  
Vol 75 (5) ◽  
pp. 2395-2422 ◽  
Author(s):  
Mohammad Shehab ◽  
Ahamad Tajudin Khader ◽  
Makhlouf Laouchedi ◽  
Osama Ahmad Alomari
Keyword(s):  
Numerical Optimization ◽  
Search Algorithm ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Global Numerical Optimization

A GENERALIZED HYBRID GENERATION SCHEME OF DIFFERENTIAL EVOLUTION FOR GLOBAL NUMERICAL OPTIMIZATION

2011 ◽  
Vol 10 (01) ◽  
pp. 35-65 ◽  
Author(s):  
WENYIN GONG ◽  
ZHIHUA CAI ◽  
LIYUAN JIA ◽  
HUI LI
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Search Space ◽  
Mutation Operator ◽  
Test Functions ◽  
Hybrid Generation ◽  
Global Numerical Optimization ◽  
De Algorithm ◽  
Speed Up ◽  
Continuous Domain

Differential evolution (DE) is a simple yet powerful evolutionary algorithm for global numerical optimization over continuous domain, which has been widely used in many areas. Although DE is good at exploring the search space, it is slow at the exploitation of the solutions. To alleviate this drawback, in this paper, we propose a generalized hybrid generation scheme, which attempts to enhance the exploitation and accelerate the convergence velocity of the original DE algorithm. In the hybrid generation scheme the operator with powerful exploitation is hybridized with the original DE operator. In addition, a self-adaptive exploitation factor is introduced to control the frequency of the exploitation operation. In order to evaluate the performance of our proposed generation scheme, two operators, the migration operator of biogeography-based optimization and the "DE/best/1" mutation operator, are employed as the exploitation operator. Moreover, 23 benchmark functions (including 10 test functions provided by CEC2005 special session) are chosen from the literature as the test suite. Experimental results confirm that the new hybrid generation scheme is able to enhance the exploitation of the original DE algorithm and speed up its convergence rate.

Hybridizing harmony search algorithm with cuckoo search for global numerical optimization

2014 ◽  
Vol 20 (1) ◽  
pp. 273-285 ◽  
Author(s):  
Gai-Ge Wang ◽  
Amir H. Gandomi ◽  
Xiangjun Zhao ◽  
Hai Cheng Eric Chu
Keyword(s):  
Numerical Optimization ◽  
Search Algorithm ◽  
Harmony Search ◽  
Cuckoo Search ◽  
Harmony Search Algorithm ◽  
Global Numerical Optimization

scholarly journals A Modified Cuckoo Search Algorithm for Data Clustering

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Nature Inspired Algorithms ◽  
Benchmark Test Functions ◽  
Optimal Cluster

Clustering of data is one of the necessary data mining techniques, where similar objects are grouped in the same cluster. In recent years, many nature-inspired based clustering techniques have been proposed, which have led to some encouraging results. This paper proposes a Modified Cuckoo Search (MoCS) algorithm. In this proposed work, an attempt has been made to balance the exploration of the Cuckoo Search (CS) algorithm and to increase the potential of the exploration to avoid premature convergence. This algorithm is tested using fifteen benchmark test functions and is proved as an efficient algorithm in comparison to the CS algorithm. Further, this method is compared with well-known nature-inspired algorithms such as Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Particle Swarm Optimization with Age Group topology (PSOAG) and CS algorithm for clustering of data using six real datasets. The experimental results indicate that the MoCS algorithm achieves better results as compared to other algorithms in finding optimal cluster centers.

A NOVEL EVOLUTIONARY ALGORITHM ENSEMBLE FOR GLOBAL NUMERICAL OPTIMIZATION

2013 ◽  
Vol 22 (04) ◽  
pp. 1350022
Author(s):  
YONGYONG NIU ◽  
ZIXING CAI ◽  
MIN JIN
Keyword(s):  
Evolutionary Algorithm ◽  
Evolutionary Computation ◽  
Numerical Optimization ◽  
Global Numerical Optimization ◽  
Covariance Matrix Adaptation ◽  
The Individual ◽  
Benchmark Test Functions ◽  
Adaptation Evolution ◽  
Better Than

In the past few years, evolutionary algorithm ensembles have gradually attracted more and more attention in the community of evolutionary computation. This paper proposes a novel evolutionary algorithm ensemble for global numerical optimization, named NEALE. In order to make a good tradeoff between the exploration and exploitation, NEALE is composed of two constituent algorithms, i.e., the composite differential evolution (CoDE) and the covariance matrix adaptation evolution strategy (CMA-ES). During the evolution, CoDE aims at probing more promising regions and refining the overall quality of the population, while the purposes of CMA-ES are to accelerate the convergence speed and to enhance the accuracy of the solutions. In addition, NEALE encourages the interaction between the constituent algorithms. In NEALE, the interaction is controlled by a predefined generation number and different interaction strategies are designed according to the features of the constituent algorithms. The performance of NEALE has been tested on 25 benchmark test functions developed for the special session on real-parameter optimization of the 2005 IEEE Congress on Evolutionary Computation (IEEE CEC2005). Compared with other state-of-the-art evolutionary algorithms and the individual constituent algorithms, NEALE performs significantly better than them.

Sign in / Sign up

Export Citation Format

Share Document