Two Subpopulations Cuckoo Search Algorithm Based on Mean Evaluation Method for Function Optimization Problems

2019 ◽  
Vol 34 (08) ◽  
pp. 2059027
Author(s):  
Jingsen Liu ◽  
Xiaozhen Liu ◽  
Yu Li
Keyword(s):  
Evaluation Method ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Function Optimization ◽  
Search Performance ◽  
Step Size ◽  
Fitness Evaluation ◽  
Mutation Strategy ◽  
The Individual

In order to better apply the cuckoo search (CS) algorithm in solving the problem of function extremum optimization, and further improve the phenomenon of low precision and slow convergence in the optimization process of algorithm, the two subpopulations CS algorithm based on mean value evaluation is proposed. On the one hand, the algorithm introduces dynamic inertia weight to adjust the lévy flight mechanism, thus dynamically constraining the moving step-size of each generation of population, so that the algorithm has certain self-adaptability. On the other hand, the algorithm changes the way of mutation in the preference random walk. First, the average fitness evaluation mechanism is used to divide the current population into two subpopulations: good and bad. Then, it adopts a directional mutation strategy for the better population, so that the individual can search purposefully. The worse population uses differential mutation mechanism of the disturbance items with the [Formula: see text]-distribution characteristics, and makes the individual to search in the best orientation of current, so as to enhance the local search performance and accelerate the convergence rate of the algorithm. Theoretical analysis proves the convergence and time complexity of the algorithm in this paper. The simulation results show that the improved algorithm has good applicability in solving the function optimization problem, and the optimization results and convergence speed have been significantly improved in the algorithm.

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.

Multi-Swarm Cuckoo Search Algorithm with Q-Learning Model

2020 ◽  
Author(s):  
Juan Li ◽  
Dan-dan Xiao ◽  
Ting Zhang ◽  
Chun Liu ◽  
Yuan-xiang Li ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Size Control ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Local Optimum ◽  
Step Size ◽  
Q Learning ◽  
Fitness Value ◽  
The Individual ◽  
Evolution Effect

Abstract As a novel swarm intelligence optimization algorithm, cuckoo search (CS) has been successfully applied to solve diverse problems in the real world. Despite its efficiency and wide use, CS has some disadvantages, such as premature convergence, easy to fall into local optimum and poor balance between exploitation and exploration. In order to improve the optimization performance of the CS algorithm, a new CS extension with multi-swarms and Q-Learning namely MP-QL-CS is proposed. The step size strategy of the CS algorithm is that an individual fitness value is examined based on a one-step evolution effect of an individual instead of evaluating the step size from the multi-step evolution effect. In the MP-QL-CS algorithm, a step size control strategy is considered as action, which is used to examine the individual multi-stepping evolution effect and learn the individual optimal step size by calculating the Q function value. In this way, the MP-QL-CS algorithm can increase the adaptability of individual evolution, and a good balance between diversity and intensification can be achieved. Comparing the MP-QL-CS algorithm with various CS algorithms, variants of differential evolution (DE) and improved particle swarm optimization (PSO) algorithms, the results demonstrate that the MP-QL-CS algorithm is a competitive swarm algorithm.

scholarly journals Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 149 ◽  
Author(s):  
Juan Li ◽  
Dan-dan Xiao ◽  
Hong Lei ◽  
Ting Zhang ◽  
Tian Tian
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Distribution Center ◽  
Step Size ◽  
Q Learning ◽  
Logistics Distribution ◽  
Center Location ◽  
The Individual ◽  
Distribution Center Location

Cuckoo search (CS) algorithm is a novel swarm intelligence optimization algorithm, which is successfully applied to solve some optimization problems. However, it has some disadvantages, as it is easily trapped in local optimal solutions. Therefore, in this work, a new CS extension with Q-Learning step size and genetic operator, namely dynamic step size cuckoo search algorithm (DMQL-CS), is proposed. Step size control strategy is considered as action in DMQL-CS algorithm, which is used to examine the individual multi-step evolution effect and learn the individual optimal step size by calculating the Q function value. Furthermore, genetic operators are added to DMQL-CS algorithm. Crossover and mutation operations expand search area of the population and improve the diversity of the population. Comparing with various CS algorithms and variants of differential evolution (DE), the results demonstrate that the DMQL-CS algorithm is a competitive swarm algorithm. In addition, the DMQL-CS algorithm was applied to solve the problem of logistics distribution center location. The effectiveness of the proposed method was verified by comparing with cuckoo search (CS), improved cuckoo search algorithm (ICS), modified chaos-enhanced cuckoo search algorithm (CCS), and immune genetic algorithm (IGA) for both 6 and 10 distribution centers.

Cuckoo Search Algorithm for Hydrothermal Scheduling Problem

2016 ◽  
pp. 367-400 ◽  
Author(s):  
Thang Trung Nguyen ◽  
Dieu Ngoc Vo
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Scheduling Problems ◽  
Short Term ◽  
Step Size ◽  
Hydrothermal Scheduling

This chapter proposes a Cuckoo Search Algorithm (CSA) and a Modified Cuckoo Search Algorithm (MCSA) for solving short-term hydrothermal scheduling (ST-HTS) problem. The CSA method is a new meta-heuristic algorithm inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds of other species for solving optimization problems. In the MCSA method, the eggs are first classified into two groups in which ones with low fitness function are put in top group whereas others with higher fitness function are put in abandoned group. In addition, an updated step size in the MCSA changes and tends to decrease as the iteration increases leading to near global optimal solution. The robustness and effectiveness of the CSA and MCSA are tested on several systems with different objective functions of thermal units. The results obtained by the CSA and MCSA are analyzed and compared have shown that the two methods are favorable for solving short-term hydrothermal scheduling problems.

scholarly journals An improved firefly algorithm with dynamic self-adaptive adjustment

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0255951
Author(s):  
Yu Li ◽  
Yiran Zhao ◽  
Yue Shang ◽  
Jingsen Liu
Keyword(s):  
High Performance ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Flower Pollination Algorithm ◽  
Step Size ◽  
Improved Firefly Algorithm ◽  
Random Step ◽  
Self Adaptive

The firefly algorithm (FA) is proposed as a heuristic algorithm, inspired by natural phenomena. The FA has attracted a lot of attention due to its effectiveness in dealing with various global optimization problems. However, it could easily fall into a local optimal value or suffer from low accuracy when solving high-dimensional optimization problems. To improve the performance of the FA, this paper adds the self-adaptive logarithmic inertia weight to the updating formula of the FA, and proposes the introduction of a minimum attractiveness of a firefly, which greatly improves the convergence speed and balances the global exploration and local exploitation capabilities of FA. Additionally, a step-size decreasing factor is introduced to dynamically adjust the random step-size term. When the dimension of a search is high, the random step-size becomes very small. This strategy enables the FA to explore solution more accurately. This improved FA (LWFA) was evaluated with ten benchmark test functions under different dimensions (D = 10, 30, and 100) and with standard IEEE CEC 2010 benchmark functions. Simulation results show that the performance of improved FA is superior comparing to the standard FA and other algorithms, i.e., particle swarm optimization, the cuckoo search algorithm, the flower pollination algorithm, the sine cosine algorithm, and other modified FA. The LWFA also has high performance and optimal efficiency for a number of optimization problems.

scholarly journals Cuckoo Search Algorithm Based on Repeat-Cycle Asymptotic Self-Learning and Self-Evolving Disturbance for Function Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Jie-sheng Wang ◽  
Shu-xia Li ◽  
Jiang-di Song
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Pso Algorithm ◽  
Cuckoo Search Algorithm ◽  
Function Optimization ◽  
Bee Colony ◽  
Disturbance Factor ◽  
Improved Cuckoo Search Algorithm ◽  
Self Learning

In order to improve convergence velocity and optimization accuracy of the cuckoo search (CS) algorithm for solving the function optimization problems, a new improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS) is proposed. A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more careful and thorough search near the bird’s nests location. In order to select a reasonable repeat-cycled disturbance number, a further study on the choice of disturbance times is made. Finally, six typical test functions are adopted to carry out simulation experiments, meanwhile, compare algorithms of this paper with two typical swarm intelligence algorithms particle swarm optimization (PSO) algorithm and artificial bee colony (ABC) algorithm. The results show that the improved cuckoo search algorithm has better convergence velocity and optimization accuracy.

scholarly journals A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning Technique

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1840
Author(s):  
Nicolás Caselli ◽  
Ricardo Soto ◽  
Broderick Crawford ◽  
Sergio Valdivia ◽  
Rodrigo Olivares
Keyword(s):  
Machine Learning ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Set Covering ◽  
Covering Problem ◽  
Parameter Configuration ◽  
Self Tuning ◽  
Problem Solvers

Metaheuristics are intelligent problem-solvers that have been very efficient in solving huge optimization problems for more than two decades. However, the main drawback of these solvers is the need for problem-dependent and complex parameter setting in order to reach good results. This paper presents a new cuckoo search algorithm able to self-adapt its configuration, particularly its population and the abandon probability. The self-tuning process is governed by using machine learning, where cluster analysis is employed to autonomously and properly compute the number of agents needed at each step of the solving process. The goal is to efficiently explore the space of possible solutions while alleviating human effort in parameter configuration. We illustrate interesting experimental results on the well-known set covering problem, where the proposed approach is able to compete against various state-of-the-art algorithms, achieving better results in one single run versus 20 different configurations. In addition, the result obtained is compared with similar hybrid bio-inspired algorithms illustrating interesting results for this proposal.

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