scholarly journals Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7542
Author(s):  
Bibi Aamirah Shafaa Emambocus ◽  
Muhammed Basheer Jasser ◽  
Aida Mustapha ◽  
Angela Amphawan
Keyword(s):  
Evolutionary Algorithms ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Low Cost ◽  
Dragonfly Algorithm ◽  
Robust Solutions ◽  
Intelligence Algorithm ◽  
Comprehensive Survey ◽  
Swarm Intelligence Algorithm

Swarm intelligence is a discipline which makes use of a number of agents for solving optimization problems by producing low cost, fast and robust solutions. The dragonfly algorithm (DA), a recently proposed swarm intelligence algorithm, is inspired by the dynamic and static swarming behaviors of dragonflies, and it has been found to have a higher performance in comparison to other swarm intelligence and evolutionary algorithms in numerous applications. There are only a few surveys about the dragonfly algorithm, and we have found that they are limited in certain aspects. Hence, in this paper, we present a more comprehensive survey about DA, its applications in various domains, and its performance as compared to other swarm intelligence algorithms. We also analyze the hybrids of DA, the methods they employ to enhance the original DA, their performance as compared to the original DA, and their limitations. Moreover, we categorize the hybrids of DA according to the type of problem that they have been applied to, their objectives, and the methods that they utilize.

scholarly journals Meerkat Clan Algorithm: A New Swarm Intelligence Algorithm

2018 ◽  
Vol 10 (1) ◽  
pp. 354
Author(s):  
Ahmed T. Sadiq Al-Obaidi ◽  
Hasanen S. Abdullah ◽  
Zied O. Ahmed
Keyword(s):  
Swarm Intelligence ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Traveling Salesman ◽  
Organizational Skills ◽  
Suricata Suricatta ◽  
Kalahari Desert ◽  
Intelligence Algorithm ◽  
Swarm Intelligence Algorithm

<p>Evolutionary computation and swarm intelligence meta-heuristics are exceptional instances that environment has been a never-ending source of creativeness. The behavior of bees, bacteria, glow-worms, fireflies and other beings have stirred swarm intelligence scholars to create innovative optimization algorithms. This paper proposes the Meerkat Clan Algorithm (MCA) that is a novel swarm intelligence algorithm resulting from watchful observation of the Meerkat (Suricata suricatta) in the Kalahari Desert in southern Africa. This animal shows an exceptional intelligence, tactical organizational skills, and remarkable directional cleverness in its traversal of the desert when searching for food. A Meerkat Clan Algorithm (MCA) proposed to solve the optimization problems through reach the optimal solution by efficient way comparing with another swarm intelligence. Traveling Salesman Problem uses as a case study to measure the capacity of the proposed algorithm through comparing its results with another swarm intelligence. MCA shows its capacity to solve the Traveling Salesman’s Problem. Its dived the solutions group to sub-group depend of meerkat behavior that gives a good diversity to reach an optimal solution. Paralleled with the current algorithms for resolving TSP by swarm intelligence, it has been displayed that the size of the resolved problems could be enlarged by adopting the algorithm proposed here.</p>

Soccer Game Optimization

2015 ◽  
pp. 1434-1469 ◽  
Author(s):  
Hindriyanto Dwi Purnomo ◽  
Hui-Ming Wee
Keyword(s):  
Evolutionary Algorithm ◽  
Information Sharing ◽  
Swarm Intelligence ◽  
Soccer Player ◽  
Optimization Problems ◽  
Optimization Method ◽  
New Method ◽  
Intelligence Algorithm ◽  
Reproduction Process ◽  
Swarm Intelligence Algorithm

A new metaheuristic algorithm is proposed. The algorithm integrates the information sharing as well as the evolution operators in the swarm intelligence algorithm and evolutionary algorithm respectively. The basic soccer player movement is used as the analogy to describe the algorithm. The new method has two basic operators; the move off and the move forward. The proposed method elaborates the reproduction process in evolutionary algorithm with the powerful information sharing in the swarm intelligence algorithm. Examples of implementations are provided for continuous and discrete problems. The experiment results reveal that the proposed method has the potential to become a powerful optimization method. As a new method, the proposed algorithm can be enhanced in many different ways such as investigating the parameter setting, elaborating more aspects of the soccer player movement as well as implementing the proposed method to solve various optimization problems.

scholarly journals Novel Swarm Intelligence Algorithm for Global Optimization and Multi-UAVs Cooperative Path Planning: Anas Platyrhynchos Optimizer

2020 ◽  
Vol 10 (14) ◽  
pp. 4821
Author(s):  
Yong Zhang ◽  
Pengfei Wang ◽  
Liuqing Yang ◽  
Yanbin Liu ◽  
Yuping Lu ◽  
...  
Keyword(s):  
Path Planning ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Local Development ◽  
Anas Platyrhynchos ◽  
Intelligent Algorithm ◽  
Intelligence Algorithm ◽  
Engineering Problems ◽  
The Mathematical Model ◽  
Swarm Intelligence Algorithm

In this study, a novel type of swarm intelligence algorithm referred as the anas platyrhynchos optimizer is proposed by simulating the cluster action of the anas platyrhynchos. Starting from the core of swarm intelligence algorithm, on the premise of the use of few parameters and ease in implementation, the mathematical model and algorithm flow of the anas platyrhynchos optimizer are given, and the balance between global search and local development in the algorithm is ensured. The algorithm was applied to a benchmark function and a cooperative path planning solution for multi-UAVs as a means of testing the performance of the algorithm. The optimization results showed that the anas platyrhynchos optimizer is more superior in solving optimization problems compared with the mainstream intelligent algorithm. This study provides a new idea for solving more engineering problems.

scholarly journals A scattering and repulsive swarm intelligence algorithm for solving global optimization problems

2018 ◽  
Vol 156 ◽  
pp. 12-42 ◽  
Author(s):  
Diptangshu Pandit ◽  
Li Zhang ◽  
Samiran Chattopadhyay ◽  
Chee Peng Lim ◽  
Chengyu Liu
Keyword(s):  
Global Optimization ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Intelligence Algorithm ◽  
Swarm Intelligence Algorithm

scholarly journals A New Compound Swarm Intelligence Algorithms for Solving Global Optimization Problems

2013 ◽  
Vol 10 (9) ◽  
pp. 2010-2020
Author(s):  
Ibrahim M. Hezam ◽  
Osama Abdel Raouf ◽  
Mohey M. Hadhoud
Keyword(s):  
Swarm Intelligence ◽  
Firefly Algorithm ◽  
Research Study ◽  
Optimization Problems ◽  
Cuckoo Search ◽  
Hybrid Swarm ◽  
Swarm Optimization ◽  
Intelligence Algorithm ◽  
Swarm Algorithms ◽  
Swarm Intelligence Algorithm

This paper proposes a new hybrid swarm intelligence algorithm that encompasses the feature of three major swarm algorithms. It combines the fast convergence of the Cuckoo Search (CS), the dynamic root change of the Firefly Algorithm (FA), and the continuous position update of the Particle Swarm Optimization (PSO). The Compound Swarm Intelligence Algorithm (CSIA) will be used to solve a set of standard benchmark functions. The research study compares the performance of CSIA with that of CS, FA, and PSO, using the same set of benchmark functions. The comparison aims to test if the performance of CSIA is Competitive to that of the CS, FA, and PSO algorithms denoting the solution results of the benchmark functions.

Soccer Game Optimization

2013 ◽  
pp. 386-420 ◽  
Author(s):  
Hindriyanto Dwi Purnomo ◽  
Hui-Ming Wee
Keyword(s):  
Evolutionary Algorithm ◽  
Information Sharing ◽  
Swarm Intelligence ◽  
Soccer Player ◽  
Optimization Problems ◽  
Optimization Method ◽  
New Method ◽  
Intelligence Algorithm ◽  
Reproduction Process ◽  
Swarm Intelligence Algorithm

A new metaheuristic algorithm is proposed. The algorithm integrates the information sharing as well as the evolution operators in the swarm intelligence algorithm and evolutionary algorithm respectively. The basic soccer player movement is used as the analogy to describe the algorithm. The new method has two basic operators; the move off and the move forward. The proposed method elaborates the reproduction process in evolutionary algorithm with the powerful information sharing in the swarm intelligence algorithm. Examples of implementations are provided for continuous and discrete problems. The experiment results reveal that the proposed method has the potential to become a powerful optimization method. As a new method, the proposed algorithm can be enhanced in many different ways such as investigating the parameter setting, elaborating more aspects of the soccer player movement as well as implementing the proposed method to solve various optimization problems.

scholarly journals A Diversity Model Based on Dimension Entropy and Its Application to Swarm Intelligence Algorithm

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 397
Author(s):  
Hongwei Kang ◽  
Fengfan Bei ◽  
Yong Shen ◽  
Xingping Sun ◽  
Qingyi Chen
Keyword(s):  
Swarm Intelligence ◽  
Optimization Problems ◽  
Early Stage ◽  
Population Diversity ◽  
Original Algorithm ◽  
Intelligence Algorithm ◽  
Problem Dimension ◽  
Self Adaptation ◽  
Diversity Control ◽  
Swarm Intelligence Algorithm

The swarm intelligence algorithm has become an important method to solve optimization problems because of its excellent self-organization, self-adaptation, and self-learning characteristics. However, when a traditional swarm intelligence algorithm faces high and complex multi-peak problems, population diversity is quickly lost, which leads to the premature convergence of the algorithm. In order to solve this problem, dimension entropy is proposed as a measure of population diversity, and a diversity control mechanism is proposed to guide the updating of the swarm intelligence algorithm. It maintains the diversity of the algorithm in the early stage and ensures the convergence of the algorithm in the later stage. Experimental results show that the performance of the improved algorithm is better than that of the original algorithm.

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