Evolutionary Global Optimization Research Articles

Synthesis of thinned planar antenna arrays using teaching–learning-based optimization

In this paper, the design of thinned planar antenna arrays of isotropic radiators with optimum side lobe level reduction is studied. The teaching–learning-based optimization (TLBO) method, a newly proposed global evolutionary optimization method, is used to determine an optimum set of turned-ON elements of thinned planar antenna arrays that provides a radiation pattern with optimum side lobe level reduction. The TLBO represents a new algorithm for optimization problems in antenna arrays design. It is shown that the TLBO provides results that are better than (or the same as) those obtained using other evolutionary algorithms.

A GPU implementation of a hybrid evolutionary algorithm: GPuEGO

The high computation requirements of global optimization algorithms, when used to solve real optimization problems, have caused the appearance of different parallelization strategies using several parallel computing architectures. In this work, the Universal Evolutionary Global Optimizer is implemented in CUDA to be run on GPU architectures (GPuEGO). This parallelization of the referred evolutionary multimodal optimization algorithm is rather different from other previous parallel implementations designed to be executed into shared or distributed memory processors. In this case, due to the special characteristics of a GPU architecture, the original data structures are not valid and it has been necessary to redefine them and all the functions that operate with them. When this approach is applied the acceleration factors achieved by GPuEGO range from $${\times }$$ × 6.33 to $${\times }$$ × 23.20 depending on the test function.

Synthesis of Non-Uniform Amplitude equally Spaced Antenna Arrays Using PSO and DE Algorithms

In recent years Evolutionary computation has its growth to extent. Synthesis of non-uniform linear antenna arrays is one of the most important electromagnetic optimization problems of the current interest. In this paper, the design of non-uniform linear antenna arrays with optimum side lobe level reduction is investigated. Two global evolutionary optimization methods are Particle Swarm Optimization algorithm(PSO) and Differential Evolution algorithm(DE ) are used to determine an optimum set of weights and positions that provide a radiation pattern with optimum side lobe level having uniform progressive phase between the elements. Simulation results show considerable enhancements in array performances using the global optimizers. The paper finally illustrates a comparative evaluation of the two proposed algorithms regarding their applicability as numerical optimization techniques.

Synthesis of thinned concentric circular antenna arrays using teaching-learning-based optimization

In this article, the design of thinned concentric circular antenna arrays CCAAs of isotropic radiators with optimum side lobe level SLL reduction is studied. The newly proposed global evolutionary optimization method; namely, the teaching-learning-based optimization TLBO is used to determine an optimum set of turned ON elements of thinned CCAAs that provides a radiation pattern with optimum SLL reduction. The TLBO represents a new algorithm for optimization problems in electromagnetics and antennas. It is shown that the TLBO provides results that are somewhat better than those obtained using other evolutionary algorithms, like the firefly algorithm and biogeography based optimization. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:443-450, 2014.

Circular antenna array synthesis using firefly algorithm

In this article, the design of circular antenna arrays (CAAs) and concentric circular antenna arrays (CCAAs) of isotropic radiators with optimum side lobe level (SLL) reduction is studied. The newly proposed global evolutionary optimization method; namely, the firefly algorithm (FA) is used to determine an optimum set of weights and positions for CAAs, and an optimum set of weights for CCAAs, that provides a radiation pattern with optimum SLL reduction with the constraint of a fixed major lobe beamwidth. The FA represents a new algorithm for optimization problems in electromagnetics. It is shown that the FA results provide a SLL reduction that is better than that obtained using well-known algorithms, like the particle swarm optimization, genetic algorithm (GA), and evolutionary programming. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:139–146, 2014.

Crystal structures and metastability of carbon-boron compounds C3B and C5B

The recent discovery of the diamondlike C${}_{3}$B and C${}_{5}$B compounds has raised hopes of revealing interesting properties and also elicits questions about the stability of such compounds. Using our implementation of the evolutionary global space-group optimization method, we have found ordered structural models for C${}_{3}$B (layered hexagonal) and C${}_{5}$B (diamondlike) with lower energies than previously obtained and revealing unusual layer-stacking sequences. The compounds are less stable than a mixture of freestanding lowest-energy phases of B, C, and C${}_{4}$B, thus C${}_{3}$B and C${}_{5}$B are not ground-state structures. Nevertheless, disordered diamondlike C${}_{3}$B and C${}_{5}$B can be formed exothermically at high temperature in the reaction [graphitelike C${}_{3}$B] $+$ 2C \ensuremath{\rightarrow} [diamondlike C${}_{5}$B] and [graphitelike C${}_{3}$B] \ensuremath{\rightarrow} [diamondlike C${}_{3}$B]. Thus, the disorder on the C and B sites of diamondlike C${}_{3}$B and C${}_{5}$B is responsible for the observed phases.

Finite element based hybrid evolutionary optimization approach to solving rigid pavement inversion problem

This paper focuses on the development of a new backcalculation method for concrete road structures based on a hybrid evolutionary global optimization algorithm, namely shuffled complex evolution (SCE). Evolutionary optimization algorithms are ideally suited for intrinsically multi-modal, non-convex, and discontinuous real-world problems such as pavement backcalculation because of their ability to explore very large and complex search spaces and locate the globally optimal solution using a parallel search mechanism as opposed to a point-by-point search mechanism employed by traditional optimization algorithms. SCE, a type of evolutionary optimization algorithms based on the tradeoff of exploration and exploitation, has proved to be an efficient method for many global optimization problems and in some cases it does not suffer the difficulties encountered by other evolutionary computation techniques. The SCE optimization approach is hybridized with a neural networks surrogate finite-element based forward pavement response model to enable rapid computation of global or near-global pavement layer moduli solutions. The proposed rigid pavement backcalculation model is evaluated using field non-destructive test data acquired from a full-scale airport pavement test facility.

An Improved Self-adaptive Control Parameter of Differential Evolution for Global Optimization

An Improved Self-adaptive Control Parameter of Differential Evolution for Global Optimization

Error Modelling and Differential-Evolution-Based Parameter Identification Method for Redundant Hybrid Robot

This paper focuses on the geometrical error modelling and parameter identification of a 10 degree-of-freedom (DOF) redundant serial—parallel hybrid intersector welding/cutting robot (IWR). The proposed hybrid robot consists of a kinematically redundant 4-DOF serial mechanism to enlarge workspace and a 6-DOF Stewart parallel robot to improve the end-effector accuracy. Due to its redundant degrees of freedom and the serial—parallel structure, the traditional error modelling and identification methods which tailored for pure serial robot or pure parallel robot cannot be directly used. In this paper, a hybrid error modelling method for redundant serial—parallel hybrid robot is presented by combining both the traditional forward calibration and inverse calibration method. Furthermore, because of the high nonlinear and multi-modal characteristics of the derived hybrid error model, the traditional iterative linear least-square algorithm cannot be utilized to identify the error parameters. In this paper, an easy-to-use and powerful evolutionary global optimization algorithm named differential evolution (DE) is employed to search for a set of optimum combination of all error parameters in the error model to minimize the discrepancies of measured and predicted leg lengths. Numerical simulation and analysis are conducted by generating random manufacturing and assembly errors within the real error parameter tolerance range. Meanwhile, different measurement poses of the end-effector and the corresponding joint displacements of the serial mechanism are also randomly generated in the workspace to simulate the real physical behaviours. The simulation results show that the DE-based parameter identification method is robust and reliable, and all of the preset errors can be successfully recovered. The simulation also shows that the hybrid calibration method can avoid the external pose measurement of the connecting point between serial and parallel mechanism, and the pose measurement of the end-effector of serial—parallel robot can satisfy the calibration purpose effectively.

Inverse estimation of inlet parameters in an axisymmetric cylindrical combustor with thermal radiation effect

The major objective of the present study is to extend the application of an inverse analysis to a more realistic engineering problem with complex combustion process than traditional simple heat transfer problem. In order to do this, unknown inlet parameters for burner in an axisymmetric cylindrical combustor, such as species mass fractions and inlet velocities of fuel/oxidizer, are estimated from measurement data using the inverse analysis. To simulate more realistic phenomena, thermal radiation effect is considered additionally. For efficient estimation in a practical diffusion-controlled turbulent combustion problem, the repulsive particle swarm optimization (RPSO) method is implemented as an inverse solver which belongs to the class of stochastic evolutionary global optimization methods. Based on the results of the present study, it is expected that useful information can be successfully predicted using the inverse analysis even in a design optimization of the real combustion systems accompanying thermal radiation effect.

Fixed or variable demand? Does it matter when locating a facility?

In most competitive location models available in the literature, it is assumed that the demand is fixed independently of market conditions. However, demand may vary depending on prices, distances to the facilities, etc., especially when the goods are not essential. Taking variable demand into consideration increases the complexity of the problem and, therefore, the computational effort needed to solve it, but it may make the model more realistic. In this paper, a new planar competitive location and design problem with variable demand is presented. By using it, it is shown numerically for the first time in the literature that the assumption of fixed demand influences the location decision very much, and therefore the selection of the type of demand (fixed or variable) must be made with care when modeling location problems. Finally, two methods are presented to cope with the new model, an exact interval branch-and-bound method and an evolutionary algorithm called UEGO ( Universal Evolutionary Global Optimizer).

확산지배 난류 연소현상에서 역해석을 이용한 CH4/O2의 초기 질량분율 추정에 관한 연구

본 연구에서는 기존의 역열전달 문제 (inverse heat transfer problem)와 같이 역해석 (inverse analysis)을 통해 미지의 파라미터를 추정(estimation)하는 개념을 복잡한 연소문제에 도입하였다. 기존의 연구에서는 역해석 기법을 연소문제 자체에 보다는 대부분 연소현상을 동반한 복사열전달과 같은 역열전달 문제에 국한해서 적용하고 있기 때문에, 열전달 문제에 한정되어 사용되고 있는 기존의 역해석을 새로운 공학문제에 확장하여 적용함과 동시에 효율적인 연소기 설계 및 최적화 개념을 제시하는데 본 연구의 의의가 있다고 할 수 있다. 이를 위해 실제적으로 많이 사용하고 있는 축대칭 원통형 연소기 내부로 주입되는 메탄(CH₄)과 산소 (O₂) 성분의 초기 질량분율 값을 연소기 입구 근방에서 측정한 개스의 온도 데이터를 이용하여 역추정하였다. 이때, 복잡한 확산지배 연소 현상을 효율적으로 역해석하기 위해 최적화 방법 중의 하나인 반발 입자 군집 최적화 방법을 역해석 기법으로 적용하였다.

Inverse estimation of inlet parameters in diffusion-controlled turbulent combustion in an axisymmetric combustor

The major objective of the present study is to extend the application of an inverse analysis to a more realistic engineering problem with complex combustion process than traditional simple heat transfer problem. In order to do this, unknown inlet parameters for burner in an axisymmetric combustor, such as species mass fractions and inlet velocities of fuel/oxidizer, are estimated from measurement data using the inverse analysis. For efficient estimation in a practical diffusion-controlled turbulent combustion problem, the repulsive particle swarm optimization (RPSO) method is implemented as an inverse solver which belongs to the class of stochastic evolutionary global optimization methods. Based on the results of the present study, it is expected that useful information can be successfully predicted using the inverse analysis even in a design optimization of the real combustion systems.

LINEAR ARRAY SYNTHESIS USING BIOGEOGRAPHY BASED OPTIMIZATION

This paper presents a novel optimization technique biogeography based optimization (BBO) for antenna array synthesis. BBO is a relatively new evolutionary global optimization technique based on the science of biogeography. It is capable of solving linear and non-linear problems. In this paper, BBO algorithm is used to determine an optimum set of amplitudes of antenna elements that provide a radiation pattern with maximum side lobe level reduction and/or null placement in the specifled directions. The results obtained show the efiectiveness of the BBO algorithm, and they are better than previous published results.

Parallel evolutionary algorithms based on shared memory programming approaches

In this work, two parallel techniques based on shared memory programming are presented. These models are specially suitable to be applied over evolutionary algorithms. To study their performance, the algorithm UEGO (Universal Evolutionary Global Optimizer) has been chosen.

Predicting stable stoichiometries of compounds via evolutionary global space-group optimization

Whereas the Daltonian atom-to-atom ratios in ordinary molecules are well understood via the traditional theory of valence, the naturally occurring stoichiometries in intermetallic compounds ${A}_{p}{B}_{q}$, as revealed by phase-diagram compilations, are often surprising. Even equal-valence elements $A$ and $B$ give rise to unequal $(p,q)$ stoichiometries, e.g., the 1:2, 2:1, and 3:1 ratios in ${\text{Al}}_{p}{\text{Sc}}_{q}$. Moreover, sometimes different stoichiometries are associated with different lattice types and hence rather different physical properties. Here, we extend the fixed-composition global space-group optimization (GSGO) approach used to predict, via density-functional calculations, fixed-composition lattice types [G. Trimarchi and A. Zunger, J. Phys.: Condens. Matter 20, 295212 (2008)] to identify simultaneously all the minimum-energy lattice types throughout the composition range. Starting from randomly selected lattice vectors, atomic positions and stoichiometries, we construct the $T=0$ ``convex hull'' of energy vs composition. Rather than repeat a set of GSGO searches over a fixed list of stoichiometries, we minimize the distance to the convex hull. This approach is far more efficient than the former one as a single evolutionary search sequence simultaneously identifies the lowest-energy structures at each composition and among these it selects those that are ground states. For Al-Sc we correctly identify the stable stoichiometries and relative structure types: ${\text{AlSc}}_{2}\text{-B}{8}_{2}$, AlSc-B2, and ${\text{Al}}_{2}\text{Sc-C}15$ in the ${N}_{at}=6$ periodic cells, and ${\text{Al}}_{2}{\text{Sc}}_{6}\text{-D}{0}_{19}$, AlSc-B2, and ${\text{Al}}_{3}\text{Sc-L}{1}_{0}$ in the ${N}_{at}=8$ periodic cells. This extended evolutionary GSGO algorithm represents a step toward a fully ab initio materials synthesis, where compounds are predicted starting from sole knowledge of the chemical species of the constituents.

Parallel algorithms for continuous competitive location problems

A continuous location problem in which a firm wants to set up a single new facility in a competitive environment is considered. Other facilities offering the same product or service already exist in the area. Both the location and the quality of the new facility are to be found so as to maximize the profit obtained by the firm. This is a hard-to-solve global optimization problem. An evolutionary algorithm called Universal Evolutionary Global Optimizer (UEGO) seems to be the best procedure to cope with it, but the algorithm needs several hours of CPU time for solving large instances. In this paper, four parallelizations of UEGO are presented. They all are coarse-grain methods which differ in their migratory policies. A computational study is carried out to compare the performance of the parallel algorithms. The results show that one of the parallelizations always gives the best objective function value and has an almost linear speed-up for up to 16 processing elements for large instances.

Application of evolutionary global optimization techniques in the design of RC water tanks

This paper presents evolutionary-based optimization procedures for designing conical reinforced concrete water tanks. The material cost of the tank that includes concrete, reinforcement, and formwork required for walls and floor was chosen as the objective function in the nonlinear optimization problem formulation. The wall thicknesses (at the bottom and at the top), base thickness, depth of water tank, and wall inclination were considered as design variables. Three advanced optimization techniques to solve the nonlinear constrained structural optimization problems were investigated. These methods are: (1) Shuffled Complex Evolution (SCE), (2) Simulated Annealing (SA) and Genetic Algorithm (GA). Several tests were performed to illustrate the robustness of these techniques and results were encouraging for SCE Method. The SCE method proved to be superior to the SA and GA methods in obtaining the best discovered solutions. The paper concludes that the robust search capability of SCE algorithm technique is well suited for solving the structural problem in hand.

A robust and efficient algorithm for planar competitive location problems

In this paper we empirically analyze several algorithms for solving a Huff-like competitive location and design model for profit maximization in the plane. In particular, an exact interval branch-and-bound method and a multistart heuristic already proposed in the literature are compared with uego (Universal Evolutionary Global Optimizer), a recent evolutionary algorithm. Both the multistart heuristic and uego use a Weiszfeld-like algorithm as local search procedure. The computational study shows that uego is superior to the multistart heuristic, and that by properly fine-tuning its parameters it usually (in the computational study, always) find the global optimal solution, and this in much less time than the interval branch-and-bound method. Furthermore, uego can solve much larger problems than the interval method.

Discerning and Modeling the Fate and Transport of Testosterone in Undisturbed Soil

Testosterone is an endocrine disruptor that is released into the environment from natural and anthropogenic sources. The objective of this study was to achieve a better understanding of the complex fate and transport of this labile compound in an undisturbed agricultural soil (a Hamar Sandy, mixed, frigid typic Endoaquolls). This was done by using batch and miscible-displacement experiments, and by using a chemical nonequilibrium transport model. Sorption and transformations of testosterone were discerned using various batch experiments. The batch experiments indicated that the aqueous phase concentrations of testosterone rapidly decreased from 12 to 15% of the initial aqueous concentration within 5 h, but then gradually increased through time and reached 28 to 29% of the initial aqueous concentration at 168 h. The increase in the aqueous concentration was explained by mineralization and biodegradation. Multiple first-order models were used to describe batch experiments where simultaneous degradation and sorption processes occurred. An evolutionary global optimization strategy was used to estimate the process parameters from these batch experiments and there was high confidence in these parameter estimates. The result of column experiments also showed that 23.4% of testosterone was mineralized to CO2 as it transported through the column. Combustion analyses of extracted soil from inside the columns showed that most of the 14C retained in the column (69-74%) was sorbed in the top 5 cm. The independently determined batch parameters were incorporated into a chemical nonequilibrium transport model, which provided an excellent description of the hormone in the effluent, and vertical redistribution in the soil column.