A novel hybrid Harris hawks-simulated annealing algorithm and RBF-based metamodel for design optimization of highway guardrails

Abstract

In this paper, a novel hybrid optimization algorithm is introduced by hybridizing a Harris hawks optimization algorithm(HHO) and simulated annealing for the purpose of accelerating its global convergence performance and optimizing structural design problems. This paper is the first research study in which the hybrid Harris hawks simulated annealing algorithm (HHOSA) is used for the optimization of design parameters for highway guardrail systems. The HHOSA is evaluated using the well-known benchmark problems such as the three-bar truss problem, cantilever beam problem, and welded beam problem. Finally, a guardrail system that has an H1 containment level as a case study is optimized to investigate the performance of the HHOSA. The guardrail systems are designed with different cross-sections and distances between the posts. TB11 and TB42 crash analyses are performed according to EN 1317 standards. Twenty-five different designs are evaluated considering weight, the guardrail working width, and the acceleration severity index (ASI). As a result of this research, the optimum design of a guardrail is obtained, which has a minimum weight and acceleration severity index value (ASI). The results show that the HHOSA is a highly effective approach for optimizing real-world design problems.

Improved Harris Hawks Optimization Based on Adaptive Cooperative Foraging and Dispersed Foraging Strategies

“…Literature [43] added interference terms to the escape energy to control the location of the disturbance peaks, which increased the global search capability in the later stage. Besides, some scholars combined with the exploration ability of other algorithms to improve HHO, such as combining sine and cosine algorithm [44], simulated annealing algorithm [45] and dragonfly algorithm [46]. However, the above improvements are generally aimed at improving exploration capabilities, and the lack of a balanced method between search capabilities makes the robustness and search results under multimodal or modern highly complex optimization tasks generally weak.…”

Optimal Design and Simulation for PID Controller Using Fractional-Order Fish Migration Optimization Algorithm

“…QUATRE is an excellent algorithm which improved the drawback of DE that did not achieve equilibrium search in search space without prior knowledge and moveover, it generalized the crossover operation of DE from vector to matrix. In algorithms based on physical or mathematical models, Simulated Annealing (SA) [38], [39] originates from the principle of solid annealing; Gravitational Search Algorithm (GSA) [40], [41] mainly uses the law of gravitation between two objects to guide the motion optimization of each particle to search for the optimal solution; Sine Cosine Algorithm (SCA) [42], [43] is achieved by iteration of sine and cosine functions.…”

Integrated optimal wind‐resistant design of super‐tall structures with toggle‐brace damper systems

“…[46][47][48][49][50][51] Based on the foregoing, certain hybrid algorithms with a higher optimization efficiency can be obtained by combining the standard meta-heuristic algorithms. [29,52,53] The selection of an appropriate optimal algorithm can improve the quality and efficiency of the optimal design. Traditional linear and nonlinear mathematical programing algorithms and meta-heuristics are suitable for determining the optimal placement, number, and damping coefficient of viscous dampers and for optimizing the structural components.…”

“…[ 5,6,10 ] According to the existing optimization principle, meta‐heuristic algorithms can be grouped into three main categories, [ 11 ] including evolutionary algorithms, [ 12–15 ] physical algorithms, [ 16–25 ] and swarm‐based algorithms. [ 26–28 ] At the beginning of the 21st century, a number of meta‐heuristic algorithms such as the gray wolf optimization algorithm, dragonfly optimization algorithm, multi‐verse optimization algorithm, grasshopper optimization algorithm, salp swarm algorithm, artificial bee colony algorithm, particle swarm optimization algorithm, and ant colony algorithm have been proposed and applied to reach global optimal solutions for solving real‐world optimization tasks [ 29–38 ] in a wide fields including topology design, [ 31,39,40 ] mechanical machining, [ 30,35,37,41 ] aerospace engineering, [ 9,42 ] automotive manufacturing industry, [ 31–34,36,38,43–45 ] and civil engineering. [ 46–51 ] Based on the foregoing, certain hybrid algorithms with a higher optimization efficiency can be obtained by combining the standard meta‐heuristic algorithms.…”

“…[ 46–51 ] Based on the foregoing, certain hybrid algorithms with a higher optimization efficiency can be obtained by combining the standard meta‐heuristic algorithms. [ 29,52,53 ]…”