In this paper, a new kind of swarm-based metaheuristic search method, called Elephant Herding Optimization (EHO), is proposed for solving optimization tasks The EHO method is inspired by the herding behavior of elephant group In nature, the elephants belonging to different clans live together under the leadership of a matriarch, and the male elephants will leave their family group when they grow up These two behaviors can be modelled into two following operators: clan updating operator and separating operator In EHO, the elephants in each clan are updated by its current position and matriarch through clan updating operator It is followed by the implementation of the separating operator which can enhance the population diversity at the later search phase To demonstrate its effectiveness, EHO is benchmarked by fifteen test cases comparing with BBO, DE and GA The results show that EHO can find the better values on most benchmark problems than those three metaheuristic algorithms

Elephant Herding Optimization

In this paper, we intend to propose a new heuristic optimization method, called animal migration optimization algorithm. This algorithm is inspired by the animal migration behavior, which is a ubiquitous phenomenon that can be found in all major animal groups, such as birds, mammals, fish, reptiles, amphibians, insects, and crustaceans. In our algorithm, there are mainly two processes. In the first process, the algorithm simulates how the groups of animals move from the current position to the new position. During this process, each individual should obey three main rules. In the latter process, the algorithm simulates how some animals leave the group and some join the group during the migration. In order to verify the performance of our approach, 23 benchmark functions are employed. The proposed method has been compared with other well-known heuristic search methods. Experimental results indicate that the proposed algorithm performs better than or at least comparable with state-of-the-art approaches from literature when considering the quality of the solution obtained.

Animal migration optimization: an optimization algorithm inspired by animal migration behavior

A survey on applications and variants of the cuckoo search algorithm

Modified cuckoo search algorithm with self adaptive parameter method

Artificial intelligence is a branch of computer science, involved in the research, design, and application of intelligent computer. Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives for efficiently solving problems in the civil engineering. This paper summarizes recently developed methods and theories in the developing direction for applications of artificial intelligence in civil engineering, including evolutionary computation, neural networks, fuzzy systems, expert system, reasoning, classification, and learning, as well as others like chaos theory, cuckoo search, firefly algorithm, knowledge-based engineering, and simulated annealing. The main research trends are also pointed out in the end. The paper provides an overview of the advances of artificial intelligence applied in civil engineering.

/pdf/artificial-intelligence-in-civil-engineering-azmfdtknbz.pdf

Artificial Intelligence in Civil Engineering

We study the parameter estimation of a nonlinear chaotic system, which can be essentially formulated as a multidimensional optimization problem In this paper, an orthogonal learning cuckoo search algorithm is used to estimate the parameters of chaotic systems This algorithm can combine the stochastic exploration of the cuckoo search and the exploitation capability of the orthogonal learning strategy Experiments are conducted on the Lorenz system and the Chen system The proposed algorithm is used to estimate the parameters for these two systems Simulation results and comparisons demonstrate that the proposed algorithm is better or at least comparable to the particle swarm optimization and the genetic algorithm when considering the quality of the solutions obtained

https://iopscience.iop.org/article/10.1088/1674-1056/21/5/050507/pdf

Yin Ming-Hao

Papers

Parameter estimation for chaotic systems using the cuckoo search algorithm with an orthogonal learning method