Hybrid particle swarm-based algorithms and their application to linear array synthesis

A heuristic particle swarm optimization (PSO) based algorithm is presented in this work and the novel hybrid approach is applied to linear array synthesis considering complex weights and directive element patterns so as to analyze its usefulness and limitations. Basically, classical PSO schemes are modified by introducing a tournament selection strategy and the downhill simplex local search method, so that the hybrid algorithms proposed combine the strengths of the PSO to initially explore the search space, the pressure exerted by the genetic selection operator to manage and speed up the search, and finally, the ability of the local optimization technique to quickly descend to the optimum solution. Four classical real-valued PSO schemes are taken as reference and synthesis results for a 60-element linear array comparing those classical schemes and the hybridized ones are reported and discussed in order to show the improvements achieved by the hybrid approaches.This work was supported by the Spanish Ministry of Science and Innovation (project number TEC2008-02730/TEC)

Enhanced Symbiotic Organisms Search (ESOS) for Global Numerical Optimization

Symbiotic organisms search (SOS) is a simple yet effective metaheuristic algorithm to solve a wide variety of optimization problems. Many studies have been carried out to improve the performance of the SOS algorithm. This research proposes an improved version of the SOS algorithm called the “enhanced symbiotic organisms search” (ESOS) for global numerical optimization. The conventional SOS is modified by implementing a new searching formula into the parasitism phase to produce a better searching capability. The performance of the ESOS is verified using 26 benchmark functions and one structural engineering design problem. The results are then compared with existing metaheuristic optimization methods. The obtained results show that the ESOS gives a competitive and effective performance for global numerical optimization

Designing Volumetric Truss Structures

We present the first algorithm for designing volumetric Michell Trusses. Our method uses a parametrization approach to generate trusses made of structural elements aligned with the primary direction of an object's stress field. Such trusses exhibit high strength-to-weight ratios. We demonstrate the structural robustness of our designs via a posteriori physical simulation. We believe our algorithm serves as an important complement to existing structural optimization tools and as a novel standalone design tool itself

A Novel Implementation of Nature-inspired Optimization for Civil Engineering: A Comparative Study of Symbiotic Organisms Search

The increasing numbers of design variables and constraints have made many civil engineering problems significantly more complex and difficult for engineers to resolve in a timely manner. Various optimization models have been developed to address this problem. The present paper introduces Symbiotic Organisms Search (SOS), a new nature-inspired algorithm for solving civil engineering problems. SOS simulates mutualism, commensalism, and parasitism, which are the symbiotic interaction mechanisms that organisms often adopt for survival in the ecosystem. The proposed algorithm is compared with other algorithms recently developed with regard to their respective effectiveness in solving benchmark problems and three civil engineering problems. Simulation results demonstrate that the proposed SOS algorithm is significantly more effective and efficient than the other algorithms tested. The proposed model is a promising tool for assisting civil engineers to make decisions to minimize the expenditure of material and financial resources

Optimal Control of an Uninhabited Loyal Wingman

As researchers strive to achieve autonomy in systems, many believe the goal is not that machines should attain full autonomy, but rather to obtain the right level of autonomy for an appropriate man-machine interaction. A common phrase for this interaction is manned-unmanned teaming (MUM-T), a subset of which, for unmanned aerial vehicles, is the concept of the loyal wingman. This work demonstrates the use of optimal control and stochastic estimation techniques as an autonomous near real-time dynamic route planner for the DoD concept of the loyal wingman. First, the optimal control problem is formulated for a static threat environment and a hybrid numerical method is demonstrated. The optimal control problem is transcribed to a nonlinear program using direct orthogonal collocation, and a heuristic particle swarm optimization algorithm is used to supply an initial guess to the gradient-based nonlinear programming solver. Next, a dynamic and measurement update model and Kalman filter estimating tool is used to solve the loyal wingman optimal control problem in the presence of moving, stochastic threats. Finally, an algorithm is written to determine if and when the loyal wingman should dynamically re-plan the trajectory based on a critical distance metric which uses speed and stochastics of the moving threat as well as relative distance and angle of approach of the loyal wingman to the threat. These techniques are demonstrated through simulation for computing the global outer-loop optimal path for a minimum time rendezvous with a manned lead while avoiding static as well as moving, non-deterministic threats, then updating the global outer-loop optimal path based on changes in the threat mission environment. Results demonstrate a methodology for rapidly computing an optimal solution to the loyal wingman optimal control problem

A new hybrid method for size and topology optimization of truss structures using modified ALGA and QPGA

Modified Augmented Lagrangian Genetic Algorithm (ALGA) and Quadratic Penalty Function Genetic Algorithm (QPGA) optimization methods are proposed to obtain truss structures with minimum structural weight using both continuous and discrete design variables. To achieve robust solutions, Compressed Sparse Row (CSR) with reordering of Cholesky factorization and Moore Penrose Pseudoinverse are used in case of non-singular and singular stiffness matrix, respectively. The efficiency of the proposed nonlinear optimization methods is demonstrated on several practical examples. The results obtained from the Pratt truss bridge show that the optimum design solution using discrete parameters is 21% lighter than the traditional design with uniform cross sections. Similarly, the results obtained from the 57-bar planar tower truss indicate that the proposed design method using continuous and discrete design parameters can be up to 29% and 9% lighter than traditional design solutions, respectively. Through sensitivity analysis, it is shown that the proposed methodology is robust and leads to significant improvements in convergence rates, which should prove useful in large-scale applications

Optimasi Ukuran Penampang Rangka Batang Baja Berdasarkan SNI 1729:2015 dengan Metode Metaheuristik Symbiotic Organisms Search

Penelitian ini menyelidiki metode metaheuristik baru bernama symbiotic organisms search (SOS) dalam mengoptimasi ukuran penampang rangka batang baja. Syarat batasan desain diadopsi dari spesifikasi untuk bangunan gedung baja struktural, SNI 1729:2015, yaitu rasio gaya terhadap kapasitas dan rasio kelangsingan batang. Lima studi kasus optimasi struktur rangka batang digunakan untuk menguji performa dari SOS. Hasil simulasi dengan metode SOS ini kemudian akan dibandingkan terhadap tiga metode metaheuristik lainnya, yaitu particle swarm optimization, differential evolution, dan teaching–learning-based optimization. Hasil penelitian menunjukkan bahwa algoritma SOS lebih superior dan mempunyai kemampuan konvergensi yang lebih baik dibandingkan dengan metode metaheuristik lainnya dalam menyelesaikan problem optimasi struktur rangka batang

Optimización de armaduras espaciales de acero utilizando algoritmos genéticos auto-adaptados : una primera aproximación.

En las últimas décadas, la optimización estructural mediante metaheurísticas ganó acogida en la comunidad científica; sin embargo, para garantizar buenos resultados se requiere una correcta selección de los parámetros de la metaheurísticas. En este trabajo se propone un algoritmo genético multi-cromosoma auto-adaptado para optimizar armaduras de acero tridimensionales. Las variables de diseño corresponden a las secciones asignadas a cada elemento en la armadura. El objetivo es la minimización del peso de la armadura, considerando desplazamientos y esfuerzos máximos como restricciones. El algoritmo propuesto se aplicó a la optimización de dos armaduras, produciendo diseños que pesan hasta un 35% menos que el mejor diseño inicial y son valores comparables al resultado obtenidos en otros trabajos. No obstante, la adaptación de los parámetros permite mayor robustez cuando se desea optimizar diferentes tipos de armadura y evita las ejecuciones del algoritmo de optimización que son necesarias para la calibración de sus parámetros

Examination of parameters used in ant colony algorithm over truss optimization

Metaheuristic optimization techniques have been used to solve engineering problems with an increasing speed for the last 30 years. Most of these algorithms have been developed by imitating a process in nature. In this study, the ant colony algorithm inspired by the natural life of ants is discussed. The ant colony algorithm requires some parameters to perform an optimization, as in other meta-heuristic algorithms. The aim of this study is to examine the effect the values of the parameters used in the ant colony algorithm on the results. For this purpose, as an exemplary problem, a study was carried out on the optimization of truss systems, one of the constrained problems frequently discussed in the literature. Appropriate values of optimum design parameters such as number of ants, pheromone update coefficient and penalty coefficient were investigated using the coded computer program. As a result of the study, the effect of the relevant parameters on the result was determined and the points to be considered in the selection of these parameters were specified

Magnetic charged system search for structural optimization

In this paper the Magnetic Charged System Search algorithm is applied to structural optimization. This algorithm uses the Biot-Savar law of electromagnetism to incorporate magnetic forces into the already existing Charged System Search algorithm and thus can be considered as an extension of it. Each search agent exerts magnetic forces on other agents based on the variation of its objective function value during its last movement. This additional force provides some additional information and enhances the performance of the Charged System Search. The efficiency of the Magnetic Charged System Search is examined by application of this algorithm to four structural optimization problems. The results are compared to those of CSS and some of the methods available in the literature