A novel meta-heuristic optimization algorithm: Thermal exchange optimization

Struct Control Health Monit

Eslamlou

2019

Summary This paper presents a two‐stage optimal sensor placement method for modal identification of structures. At the first stage, using a graph theoretical technique, the structure is partitioned into equal substructures. At the second stage, a preset number of triaxial sensors are proportionately allocated to the substructures. The location of sensors is determined using an evolutionary optimization algorithm, which optimizes the triaxial modal assurance criterion of the structure. The first stage leads to the even distribution of the sensors. This stage not only improves the mode shape visualization as the secondary criterion but also accelerates the optimization process by space reduction. Here, various graph‐theoretical methods including k‐means, k‐means++, and spectral partitioning are examined as the partitioning techniques. In addition, a dynamic version for quantum‐inspired evolutionary optimization algorithm (DQEA) is proposed and applied to find the placement of triaxial sensors, along with the standard version of quantum‐inspired evolutionary algorithm and genetic algorithm. In order to examine the efficiency of the methods, the bridge model of the University of Central Florida, USA, is considered as the benchmark structure. The results show that the proposed method efficiently satisfies both criteria. Moreover, the introduced optimization algorithm (DQEA) outperforms other algorithms.

Section: Optimization Methodsmentioning

confidence: 99%

“…The neighbor solutions of good individuals should be properly exploited to reach a suitable intensification. To generate the solution next to the good ones, they must be saved . In QEA, the best solutions obtained by the j th individual until the t th generation are stored in

b_{j}^{t}

.…”

Section: Optimization Methodsmentioning

confidence: 99%

An efficient two-stage method for optimal sensor placement using graph-theoretical partitioning and evolutionary algorithms

Struct Control Health Monit

Eslamlou

2019

“…TWO is a population-based metaheuristic algorithm, which is introduced by Kaveh and Zolghadr [15]. This approach models each candidate solution…”

Section: Tug Of War Optimization Algorithmmentioning

confidence: 99%

“…This algorithm also utilizes a mechanism to escape from local optima. A multi-agent metaheuristic algorithm so called tug of war optimization is introduced by Kaveh and Zolghadr [15]. This technique, models each candidate solution as a team engaged in a series of tug of war competitions.…”

Section: Introductionmentioning

confidence: 99%

Profile and wavefront optimization by metaheuristic algorithms for efficient finite element analysis

Bijari

2018

Scientia Iranica

Self Cite

For an efficient solution of the equations arising from finite element analysis, the stiffness matrix of the model should be structured. This can be done by reducing the profile or wavefront of the corresponding graph matrix of the structure depending on whether skyline or frontal method being used, respectively. One of the efficient methods to achieve this goal is the use of the method of King, extended by Sloan. In this paper the coefficients of the priority function utilized in the generalized Sloan's method are optimized using the recently developed metaheuristic algorithm, so-called vibrating particles system. The results are compared to those of other metaheuristic algorithms consisting of the particle swarm optimization, colliding bodies optimization, enhanced colliding bodies optimization, and tug of war optimization. These metaheuristics, are used for optimum nodal numbering of the graph models of the finite element meshes to reduce the profile and wavefront of the corresponding sparse matrices. Comparison of the results achieved by these metaheuristic algorithms and those of the King and Sloan, demonstrates the efficiency of the new metaheuristic utilized for profile and wavefront optimization.

“…Meta-heuristic algorithms are widely used as robust tools for structural optimization. Some of these can be listed as: Genetic Algorithms (GA) [3], Particle Swarm Optimization (PSO) [4], Charged System Search algorithm (CSS) [5], Krill-herd algorithm (KA) [6], Ray Optimization (RO) [7,8], Dolphin Echolocation Optimization (DEO) [2,9], Colliding Bodies Optimization (CBO) [10], Enhanced Colliding Bodies Optimization algorithm (ECBO) [11], Natural Forest Regeneration algorithm (NFR) [12], Water Evaporation Optimization (WEO) [13], Tug of War Optimization (TWO) [14], Gray Wolf Optimizer (GWO) [15], Ant Lion Optimizer (ALO) [16], Simplified Dolphin Echolocation algorithm (SDEA) [17].…”

Section: Introductionmentioning

confidence: 99%

Modified Dolphin Monitoring Operator for Weight Optimization of Frame Structures

Period. Polytech. Civil Eng.

Vaez²,

Hosseini³

2017

In this article, a modified dolphin monitoring (MDM) operator is introduced and used to improve the performance of the colliding bodies optimization (CBO) algorithm for optimal design of steel structures (CBO-MDM). The performance of the CBO, enhanced colliding bodies optimization (ECBO) and CBO- [17].In this study, weight optimization of frame structures is examined. Sections are selected from a standard set of steel sections such as American Institute of Steel Construction (AISC) wide-flange W-shapes. Benchmarks problems of this paper are optimized by the CBO and the ECBO algorithm. In this paper, Modified Dolphin Monitoring (MDM) is introduced and it is shown how it can be improve the performance of the CBO algorithm. MDM is a modified version of the Dolphin Monitoring (DM), in this modified version some new procedures are used to augment the DM ability to reach the global search and prevent entrapment in local optima. Results show that the performance of CBO-MDM algorithm is improved in comparison to the CBO and ECBO algorithms. This paper is organized as follow: After this introductory section, a brief explanation of the CBO and ECBO algorithms is provided in section 2. Modified dolphin monitoring is presented in section 3. In section 4, the formulations of the strength constraints of AISC load and resistance factor design specifications and displacement constraints are provided. Section 5 includes three well-known benchmark problems. Concluding remarks are presented in the final section.