• Structural Engineering and Mechanics
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• v.61 no.3
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• pp.359-370
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• 2017

This study presents a particle swarm optimization algorithm integrated with weighted particle concept and improved fly-back technique. The rationale behind this integration is to utilize the affirmative properties of these new terms to improve the search capability of the standard particle swarm optimizer. Improved fly-back technique introduced in this study can be a proper alternative for widely used penalty functions to handle existing constraints. This technique emphasizes the role of the weighted particle on escaping from trapping into local optimum(s) by utilizing a recursive procedure. On the other hand, it guaranties the feasibility of the final solution by rejecting infeasible solutions throughout the optimization process. Additionally, in contrast with penalty method, the improved fly-back technique does not contain any adjustable terms, thus it does not inflict any extra ad hoc parameters to the main optimizer algorithm. The improved fly-back approach, as independent unit, can easily be integrated with other optimizers to handle the constraints. Consequently, to evaluate the performance of the proposed method on solving the truss weight minimization problems with discrete variables, several benchmark examples taken from the technical literature are examined using the presented method. The results obtained are comparatively reported through proper graphs and tables. Based on the results acquired in this study, it can be stated that the proposed method (integrated particle swarm optimizer, iPSO) is competitive with other metaheuristic algorithms in solving this class of truss optimization problems.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.467-473
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• 2007

In this paper, we present a position-based robot hand tracking scheme where a pan-tilt camera is controlled such that a robot hand is always shown in the center of an image frame. We calculate the rotation angles of a pan-tilt camera by transforming the coordinate systems. In order to identify the model parameters, we applied two optimization techniques: a nonlinear least square optimizer and a particle swarm optimizer. From the simulation results, it is shown that the considered parameter identification problem is characterized by a highly multimodal landscape; thus, a global optimization technique such as a particle swarm optimization could be a promising tool to identify the model parameters of a robot hand tracking system, whereas the nonlinear least square optimizer often failed to find an optimal solution even when the initial candidate solutions were selected close to the true optimum.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.493-495
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• 2005

This paper proposes a swarm-based fuzzy system modeling technique for coke oven combustion control diagnosis. The coke plant produces coke for the blast furnace plant in steel making process by charging coal into oven and supplying gas to carbonize it. A conventional mathematical model for coke oven combustion control has been used to control the amount of gas input, but it does not work well because of highly nonlinear feature of coke plant. To solve this problem, swarm-based fuzzy system modeling technique is suggested to construct a diagnosis model of coke oven combustion control. Based on the measured input-output data pairs, the fuzzy rules are generated and the parameters are tuned by the PSO(Particle Swarm Optimizer) to increase the accuracy of the fuzzy system is operated. This system computes the proper amount of gas input taking the operation conditions of coke oven into account, and compares the computed result with the supplied gas input.

• Journal of Distribution Science
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• v.13 no.6
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• pp.11-15
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• 2015

Purpose - Robot path planning, a constrained optimization problem, has been an active research area with many methods developed to tackle it. This study proposes the use of a Rapidly-exploring Random Tree and Particle Swarm Optimizer algorithm for path planning. Research design, data, and methodology - The grid method is built to describe the working space of the mobile robot, then the Rapidly-exploring Random Tree algorithm is applied to obtain the global navigation path and the Particle Swarm Optimizer algorithm is adopted to obtain the best path. Results - Computer experiment results demonstrate that this novel algorithm can rapidly plan an optimal path in a cluttered environment. Successful obstacle avoidance is achieved, the model is robust, and performs reliably. The effectiveness and efficiency of the proposed algorithm is demonstrated through simulation studies. Conclusions - The findings could provide insights to the validity and practicability of the method. This method makes it is easy to build a model and meet real-time demand for mobile robot navigation with a simple algorithm, which results in a certain practical value for distribution environments.

• Advances in Computational Design
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• v.2 no.3
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• pp.195-210
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• 2017

Simultaneous optimization of trusses which concurrently takes into account design variables related to the size, shape and topology of the structure is recognized as highly complex optimization problems. In this class of optimization problems, it is possible to encounter several unstable mechanisms throughout the solution process. However, to obtain a feasible solution, these unstable mechanisms somehow should be rejected from the set of candidate solutions. This study proposes triangular unit based method (TUBM) instead of ground structure method, which is conventionally used in the topology optimization, to decrease the complexity of search space of simultaneous optimization of the planar truss structures. TUBM considers stability of the triangular units for 2 dimensional truss systems. In addition, integrated particle swarm optimizer (iPSO) strengthened with robust technique so called improved fly-back mechanism is employed as the optimizer tool to obtain the solution for these class of problems. The results obtained in this study show the applicability and efficiency of the TUBM combined with iPSO for the simultaneous optimization of planar truss structures.

• Structural Engineering and Mechanics
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• v.47 no.4
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• pp.513-530
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• 2013

In this paper, an improved version of particle swarm optimization based optimum design algorithm (IPSO) is presented for the steel grillage systems. The optimum design problem is formulated considering the provisions of American Institute of Steel Construction concerning Load and Resistance Factor Design. The optimum design algorithm selects the appropriate W-sections for the beams of the grillage system such that the design constraints are satisfied and the grillage weight is the minimum. When an improved version of the technique is extended to be implemented, the related results and convergence performance prove to be better than the simple particle swarm optimization algorithm and some other metaheuristic optimization techniques. The efficiency of different inertia weight parameters of the proposed algorithm is also numerically investigated considering a number of numerical grillage system examples.

• Smart Structures and Systems
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• v.26 no.3
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• pp.263-275
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• 2020

This research is dedicated to slope stability analysis using novel intelligent models. By coupling a neural network with spotted hyena optimizer (SHO), salp swarm algorithm (SSA), shuffled frog leaping algorithm (SFLA), and league champion optimization algorithm (LCA) metaheuristic algorithms, four predictive ensembles are built for predicting the factor of safety (FOS) of a single-layer cohesive soil slope. The data used to develop the ensembles are provided from a vast finite element analysis. After creating the proposed models, it was observed that the best population size for the SHO, SSA, SFLA, and LCA is 300, 400, 400, and 200, respectively. Evaluation of the results showed that the combination of metaheuristic and neural approaches offers capable tools for estimating the FOS. However, the SSA (error = 0.3532 and correlation = 0.9937), emerged as the most reliable optimizer, followed by LCA (error = 0.5430 and correlation = 0.9843), SFLA (error = 0.8176 and correlation = 0.9645), and SHO (error = 2.0887 and correlation = 0.8614). Due to the high accuracy of the SSA in properly adjusting the computational parameters of the neural network, the corresponding FOS predictive formula is presented to be used as a fast yet accurate substitution for traditional methods.

• Structural Engineering and Mechanics
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• v.69 no.6
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• pp.667-676
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• 2019

Minimizing the stress concentration around hypotrochoid hole in finite metallic plates under in-plane loading is an important consideration in engineering design. In the analysis of finite metallic plate, the effective factors on stress distribution around holes include curvature radius of the corner of the hole, hole orientation, plate's aspect ratio, and hole size. This paper aims to investigate the impact of these factors on stress analysis of finite metallic plate with central hypotrochoid hole. To obtain the lowest value of stress around a hypotrochoid hole, a swarm intelligence optimization method named ant lion optimizer is used. In this study, with the hypothesis of plane stress circumstances, analytical solution of Muskhelishvili's complex variable method and conformal mapping is employed. The plate is taken into account to be finite, isotropic and linearly elastic. By applying suitable boundary conditions and least square boundary collocation technique, undefined coefficients of stress function are found. The results revealed that by choosing the above-mentioned factor correctly, the lowest value of stress would be obtained around the hole allowing to an increment in load-bearing capacity of the structure.

• Structural Engineering and Mechanics
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• v.50 no.3
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• pp.323-347
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• 2014

This paper proposes a Particle Swarm Optimization (PSO) algorithm, which is improved by making use of the Harmony Search (HS) approach and called HS-PSO algorithm. A computer code is developed for optimal sizing design of non-linear steel frames with various semi-rigid and rigid beam-to-column connections based on the HS-PSO algorithm. The developed code selects suitable sections for beams and columns, from a standard set of steel sections such as American Institute of Steel Construction (AISC) wide-flange W-shapes, such that the minimum total cost, which comprises total member plus connection costs, is obtained. Stress and displacement constraints of AISC-LRFD code together with the size constraints are imposed on the frame in the optimal design procedure. The nonlinear moment-rotation behavior of connections is modeled using the Frye-Morris polynomial model. Moreover, the P-${\Delta}$ effects of beam-column members are taken into account in the non-linear structural analysis. Three benchmark design examples with several types of connections are presented and the results are compared with those of standard PSO and of other researches as well. The comparison shows that the proposed HS-PSO algorithm performs better both than the PSO and the Big Bang-Big Crunch (BB-BC) methods.

• Advances in aircraft and spacecraft science
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• v.6 no.5
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• pp.349-369
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• 2019

In the design of flight control systems there are issues that deserve special consideration and attention such as external perturbations or systems failures. A Simple Adaptive Controller (SAC) that does not require a-priori knowledge of the faults is proposed in this paper with the aim of realizing a fault tolerant flight control system capable of leading the pitch motion of an aircraft. The main condition for obtaining a stable adaptive controller is the passivity of the plant; however, since real systems generally do not satisfy such requirement, a properly defined Parallel Feedforward Compensator (PFC) is used to let the augmented system meet the passivity condition. The design approach used in this paper to synthesize the PFC and to tune the invariant gains of the SAC is the Population Decline Swarm Optimization ($P_DSO$). It is a modification of the Particle Swarm Optimization (PSO) technique that takes into account a decline demographic model to speed up the optimization procedure. Tuning and flight mechanics results are presented to show both the effectiveness of the proposed $P_DSO$ and the fault tolerant capability of the proposed scheme to control the aircraft pitch motion even in presence of elevator failures.