A comparative study on prominent nature inspired algorithms for function optimization

2016 ◽  
Author(s):  
Md. Julfikar Islam ◽  
Md. Siddiqur Rahman Tanveer ◽  
M. A. H. Akhand
Keyword(s):  
Comparative Study ◽  
Function Optimization ◽  
Nature Inspired Algorithms

scholarly journals A Comparative Study of Common Nature-Inspired Algorithms for Continuous Function Optimization

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 874
Author(s):  
Zhenwu Wang ◽  
Chao Qin ◽  
Benting Wan ◽  
William Wei Song
Keyword(s):  
Comparative Study ◽  
Heuristic Algorithms ◽  
Black Box ◽  
Function Optimization ◽  
Great Effort ◽  
Research Directions ◽  
Contrastive Study ◽  
Comprehensive Survey ◽  
Common Nature ◽  
Nature Inspired Algorithms

Over previous decades, many nature-inspired optimization algorithms (NIOAs) have been proposed and applied due to their importance and significance. Some survey studies have also been made to investigate NIOAs and their variants and applications. However, these comparative studies mainly focus on one single NIOA, and there lacks a comprehensive comparative and contrastive study of the existing NIOAs. To fill this gap, we spent a great effort to conduct this comprehensive survey. In this survey, more than 120 meta-heuristic algorithms have been collected and, among them, the most popular and common 11 NIOAs are selected. Their accuracy, stability, efficiency and parameter sensitivity are evaluated based on the 30 black-box optimization benchmarking (BBOB) functions. Furthermore, we apply the Friedman test and Nemenyi test to analyze the performance of the compared NIOAs. In this survey, we provide a unified formal description of the 11 NIOAs in order to compare their similarities and differences in depth and a systematic summarization of the challenging problems and research directions for the whole NIOAs field. This comparative study attempts to provide a broader perspective and meaningful enlightenment to understand NIOAs.

A comparative study of teaching-learning-self-study algorithms on benchmark function optimization

2017 ◽  
Vol 34 (3) ◽  
pp. 628-641 ◽  
Author(s):  
Hyun-Jun Cho ◽  
Faisal Ahmed ◽  
Tae Young Kim ◽  
Beom Seok Kim ◽  
Yeong-Koo Yeo
Keyword(s):  
Comparative Study ◽  
Function Optimization ◽  
Benchmark Function ◽  
Self Study ◽  
Teaching Learning

A Novel Hybrid Genetic Algorithm for Unconstrained and Constrained Function Optimization

2021 ◽  
pp. 148-180
Author(s):  
Rajashree Mishra ◽  
Kedar Nath Das
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Real Life ◽  
Function Optimization ◽  
Bacterial Foraging Optimization ◽  
E Coli ◽  
Application Problem ◽  
Active Interest ◽  
Nature Inspired Algorithms

During the past decade, academic and industrial communities are highly interested in evolutionary techniques for solving optimization problems. Genetic Algorithm (GA) has proved its robustness in solving all most all types of optimization problems. To improve the performance of GA, several modifications have already been done within GA. Recently GA has been hybridized with many other nature-inspired algorithms. As such Bacterial Foraging Optimization (BFO) is popular bio inspired algorithm based on the foraging behavior of E. coli bacteria. Many researchers took active interest in hybridizing GA with BFO. Motivated by such popular hybridization of GA, an attempt has been made in this chapter to hybridize GA with BFO in a novel fashion. The Chemo-taxis step of BFO plays a major role in BFO. So an attempt has been made to hybridize Chemo-tactic step with GA cycle and the algorithm is named as Chemo-inspired Genetic Algorithm (CGA). It has been applied on benchmark functions and real life application problem to prove its efficacy.

A Novel Hybrid Genetic Algorithm for Unconstrained and Constrained Function Optimization

2017 ◽  
pp. 230-268 ◽  
Author(s):  
Rajashree Mishra ◽  
Kedar Nath Das
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Real Life ◽  
Function Optimization ◽  
Bacterial Foraging Optimization ◽  
E Coli ◽  
Application Problem ◽  
Active Interest ◽  
Nature Inspired Algorithms

During the past decade, academic and industrial communities are highly interested in evolutionary techniques for solving optimization problems. Genetic Algorithm (GA) has proved its robustness in solving all most all types of optimization problems. To improve the performance of GA, several modifications have already been done within GA. Recently GA has been hybridized with many other nature-inspired algorithms. As such Bacterial Foraging Optimization (BFO) is popular bio inspired algorithm based on the foraging behavior of E. coli bacteria. Many researchers took active interest in hybridizing GA with BFO. Motivated by such popular hybridization of GA, an attempt has been made in this chapter to hybridize GA with BFO in a novel fashion. The Chemo-taxis step of BFO plays a major role in BFO. So an attempt has been made to hybridize Chemo-tactic step with GA cycle and the algorithm is named as Chemo-inspired Genetic Algorithm (CGA). It has been applied on benchmark functions and real life application problem to prove its efficacy.

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