Chaotic enhanced colliding bodies optimization algorithm for structural reliability analysis

Abstract

It is of extreme importance to assess the failure probability and safety level of structural system in structural design. Nowadays, many researchers presented several approaches for structural reliability analysis, such as the first-order reliability method, Monte Carlo simulation, and the meta-heuristic algorithm. The meta-heuristic algorithm is not only efficient to solve global optimization problems but also shown to be an effective tool for structural reliability analysis. A recent meta-heuristic optimization approach, enhanced colliding bodies optimization, has emerged as a relatively simple implementation with a fast convergence speed. Chaos theory is characterized by its ergodicity, pseudo-randomness, and irregularity. This article thus presents a novel approach introducing chaotic maps into the enhanced colliding bodies optimization algorithm to promote the performance of convergence, named as chaotic enhanced colliding bodies optimization algorithm. The proposed algorithm uses chaotic maps to change the generation pattern of particles and improve convergence characteristics. A procedure based on the effective use of the represented chaotic enhanced colliding bodies optimization is then applied in structural reliability analysis. A variety of numerical and structural problems are tested in this article to demonstrate that the given method actually improves the performance of enhanced colliding bodies optimization in convergence as well as the accuracy for reliability analysis compared with the other methods existing in the literature.

MECSBO: Multi‐strategy enhanced circulatory system based optimisation algorithm for global optimisation and reliability‐based design optimisation problems

“…Inspired by ref. [51], the chaos method can be introduced in the CSBO algorithm to achieve a better initialisation stage.…”

“…To avert these cycles and points in the tent chaotic sequence during iteration, a random variable can be introduced to the original tent chaotic mapping expression. The improved tent chaotic mapping expression is shown in Equation ( 9) [51]:…”

“…Chaos has the sensitivity property, which is easy to be influenced by its initial condition and parameter. Therefore, a slight change in the initial conditions will bring to a huge difference in its long‐term behaviour [51]. Based on the property, it can be utilised to design the initialisation of the population to make the distribution more even, which can produce more opportunities to generate possible optimal solutions.…”

Cicada Swarm Optimization: A New Method for Optimizing Persistent Problems

“…The population-based optimization algorithm is mainly inspired by the social behavior of animal groups, while the physical phenomenon based method mainly imitates the physical rules of the universe. These methods can be summarized into: Particle Swarm Optimization (PSO) [10]; which is inspired by the social behaviors of animals, such as birds and fishes , Genetic algorithm (GA) [11];which is derived from the genetic law and reproduction is indeed based on the Darwin's theory, Ant Colony Optimization (ACO) [12]; which uses the seeking behavior of the ants , Firefly Algorithm (FA) [13]; which is modelled by observation of the flicker fireflies ,Charged System Search (CSS) [14]; based on some principles from physics and mechanics which each agent is a Charged Particle, Artificial Chemical Reaction Optimization Algorithm (ACROA) [15]; based on chemical reactions possess, Ray Optimization (RO) [16] ; in which each factor is considered as a beam of light and moves in the search space to find the optimum point, Colliding Bodies Optimization (CBO) [17]; which is based on one-dimensional collisions between bodies , Crow Search Algorithm (CSA) [18]; which works based on intelligent behaviors of crows , Kidney-inspired Algorithm (KA) [19]; which uses the kidney process in the human body, Optimal Foraging Algorithm (OFA) [20]; which is inspired by the animal Behavioral Ecology Theory ,Grasshopper Optimization Algorithm (GOA) [21];which is mimics the behavior of Grasshopper insect ,and Rain optimization algorithm (ROA) [22]; which is inspired by the raindrops.…”

Accurate Photovoltaic Models Based on an Adaptive Opposition Artificial Hummingbird Algorithm

“…Different modified algorithms have been proposed in studies such as [23][24][25][26]. The AHA is one of the recently proposed algorithms, inspired by the special flight abilities of hummingbirds and their intelligent foraging strategies [27].…”