Water strider algorithm: A new metaheuristic and applications

In the last decade, we observe an increasing number of nature-inspired optimization algorithms, with authors often claiming their novelty and their capabilities of acting as powerful optimization techniques. However, a considerable number of these algorithms do not seem to draw inspiration from nature or to incorporate successful tactics, laws, or practices existing in natural systems, while also some of them have never been applied in any optimization field, since their first appearance in literature. This paper presents some interesting findings that have emerged after the extensive study of most of the existing nature-inspired algorithms. The need for irrationally introducing new nature inspired intelligent (NII) algorithms in literature is also questioned and possible drawbacks of NII algorithms met in literature are discussed. In addition, guidelines for the development of new nature-inspired algorithms are proposed, in an attempt to limit the misleading appearance of variation of metaheuristics as nature inspired optimization algorithms.

Nature inspired optimization algorithms or simply variations of metaheuristics

This study investigated operational and structural health monitoring (SHM) as well as damage evaluations for building structures. The study involved damage detection and the assessment of buildings by placing sensors and by assuming weak areas, and considered situations of assessment and self-monitoring. From this perspective, advanced sensor technology and data acquisition techniques can systematically monitor a building in real time. Furthermore, the structure’s response and behavior were observed and recorded to predict the damage to the building. In this paper, we discuss the real-time monitoring and response of buildings, which includes both static and dynamic analyses along with numerical simulation studies such as finite element analysis (FEA), and recommendations for the future research and development of SHM are made.

Practical Implementation of Structural Health Monitoring in Multi-Story Buildings

A comprehensive comparison of recent developed meta-heuristic algorithms for streamflow time series forecasting problem

In the present article, an advanced charged system search (ACSS) algorithm is designed for optimizing the large-scale frame structures using box-shaped sections for columns. The proposed ACSS is an extended version of the charged system search (CSS) which is a metaheuristic algorithm that uses the electrostatics and Newtonian laws of mechanics under the conditions of the Coulomb law. Two large-scale 3D frames with 1026 and 1935 components are optimized using AISC-LRFD to show the efficiency of using the box-shaped sections. Overall performance of the ACSS algorithm with box-shaped sections is compared to those of the upper bound strategy for integrated versions of the standard Big Bang Big Church algorithm and two of its newly developed variants. The results show the successful performance of using steel box-shaped columns in comparison to the frames with I-shaped sections. The numerical results show that the ACSS is efficient and robust compared to its standard version.

Optimal design of large-scale frames with an advanced charged system search algorithm using box-shaped sections

Optimal placement of triaxial accelerometers using hypotrochoid spiral optimization algorithm for automated monitoring of high-rise buildings

Prediction and multi-objective optimization of mechanical, economical, and environmental properties for strain-hardening cementitious composites (SHCC) based on automated machine learning and metaheuristic algorithms

The primary aim of this article is to present a new improved version of the spiral optimization algorithm (SPO) for shape and size optimization of truss structures under multiple frequency constraints. In the proposed method, which is named as hypotrochoid spiral optimization algorithm (HSPO), the movement operation of search points is redesigned, and mechanisms are utilized to boost the potency of the method in escaping from local optima and improving the accuracy of the results. Furthermore, a meta-optimization approach is developed by hybridizing the particle swarm optimization and the proposed technique for finding optimal parameter settings of the presented method. Thereafter, exploration and exploitation abilities of HSPO and SPO are compared by solving five benchmark design problems. Numerical results indicate that the convergence speed is enhanced, especially in the exploitation phase of the HSPO. The optimum designs found by the improved version are highly competitive with the best solutions reported in the literature.

Hypotrochoid spiral optimization approach for sizing and layout optimization of truss structures with multiple frequency constraints

Auto-tune learning framework for prediction of flowability, mechanical properties, and porosity of ultra-high-performance concrete (UHPC)

http://scientiairanica.sharif.edu/article_20833_28d9d491b98de739345a9f2b7c1131ef.pdf

Soroush Mahjoubi

Papers

Optimal placement of triaxial accelerometers using hypotrochoid spiral optimization algorithm for automated monitoring of high-rise buildings

Prediction and multi-objective optimization of mechanical, economical, and environmental properties for strain-hardening cementitious composites (SHCC) based on automated machine learning and metaheuristic algorithms

Hypotrochoid spiral optimization approach for sizing and layout optimization of truss structures with multiple frequency constraints

Auto-tune learning framework for prediction of flowability, mechanical properties, and porosity of ultra-high-performance concrete (UHPC)

Lion Pride Optimization Algorithm: a meta-heuristic method for global optimization problems