# Orthogonal Genetic Algorithm for Planar Thinned Array Designs

Authors: Li Zhang; Yong-Chang Jiao; Bo Chen; Hong Li
Journal: International Journal of Antennas and Propagation
 (2012)

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## Abstract

An orthogonal genetic algorithm (OGA) is applied to optimize the planar thinned array with a minimum peak sidelobe level. The method is a genetic algorithm based on orthogonal design. A crossover operator formed by the orthogonal array and the factor analysis is employed to enhance the genetic algorithm for optimization. In order to evaluate the performance of the OGA, 20×10-element planar thinned arrays have been designed to minimize peak sidelobe level. The optimization results by the OGA are better than the previously published results.


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## Body

## 1. Introduction

Aperiodic thinned array is of great importance in practical applications because of the reduction of the array costs, weight, and power consumption so that it is very active topic in electromagnetics (EM) community [1–7]. However, such advantages usually come at the cost of a loss of sidelobe level and gain compared to the filled array. In the past, several methods [1, 2] had been employed to overcome these drawbacks. However, the synthesis problem is complex and cannot be well solved with these methods. In the 1990’s, cyclic difference set (CDS) was applied to design massively thinned arrays [3]. In recently years, many global optimization algorithms have been introduced to design thinned arrays, such as genetic algorithm (GA) [4, 5], a hybrid approach based on genetic algorithm (GA) and difference sets (CDS) [6], and ant colony optimization (ACO) [7]. These evolutionary algorithms have been used to arrive at thinned arrays featuring a specified peak sidelobe level (PSLL).Orthogonal design, an experimental design method, has been applied to enhance evolutionary algorithm for optimization [8–11]. Zhang and Leung [8] proposed an orthogonal genetic algorithm by applying orthogonal design to enhance the crossover operator in genetic algorithm for multimedia multicast routing problems. To solve multidimensional knapsack problems, Li et al. [11] present a new genetic algorithm, in which the orthogonal design with factor analysis is incorporated into the genetic algorithm. Numerical results in [8, 11] demonstrate that these methods are an efficient and effective technique for solving 0-1 integer programming problems.In this paper, an orthogonal genetic algorithm (OGA) is utilized to optimize the planar thinned array with a minimum PSLL. The method is a genetic algorithm based on orthogonal design. A crossover operator formed by the orthogonal array and the factor analysis is employed to enhance the genetic algorithm for optimization. The optimization of a thinned array can be formulated as a 0-1 integer optimization problem. The OGA adopts binary code so that it is very suitable for solving 0-1 integer programming problems. 20×10-element planar thinned arrays are designed to demonstrate the effectiveness of the OGA. Numerical results show that the OGA yields better results than previously published results.


## 2. Orthogonal Experiment Design with Factor Analysis

In this section, we briefly introduce the concept of experimental design methods. For more details, the readers can refer to [8, 9]. To aid explanation, we consider a simple example. The yield of a chemical product depends on three variables: temperature, time, and amount of catalyst at the values shown in Table 1. The temperature, time, and amount of catalyst are called the factors of the experiment. Each factor has two possible values, and we say that each factor has two levels.Table 1
An experiment design problem with three factors and two levels per factor.
FactorsThe first levelThe second levelTemperature80°C85°CTime10 sec20 secAmount of catalyst1 mg2 mgTo find the best combination of levels for maximum yield, we can do one experiment for every combination and then select the best one. In the above example, there are2×2×2=8 combinations, and hence there are 8 experiments. In general, when there are N factor at Q levels, the number of combinations is QN. When N and Q are small, it may be feasible to test all the combinations. When N and Q are large, it may not be possible to test all the combinations. Therefore, it is desirable to sample a small, but representative set of combinations for experimentation. The orthogonal design was developed for this purpose.The orthogonal design provides a series of orthogonal arrays for differentN and Q. We let LM(QN) denote an orthogonal array for N factors and Q levels, where L stands for Latin square and M represents a combination of levels. For convenience, we denote LM(QN)=[ai,j]M×N, where the jth factor in the ith combination has level ai,j and ai,j∈{1,2,…,Q}. The following is an orthogonal array:(1)L4(23)=[ai,j]4×3=[111122212221].InL4(23), there are three factors, two levels per factor, and four combinations of levels. In the first combination, the three factors have respective levels 1, 1, 1; in the second combination, the three factors have respective levels 1, 2, 2, and so forth. Based on L4(23), we get a sample of 4 combinations out of 23 combinations. In this example, there are 8 combinations of levels. We apply the orthogonal array L4(23) to select four representative combinations to be tested, and these four combinations are shown in Table 2.Table 2
Based on theL4(23), 4 combinations are selected form 8 combinations for experiments.
CombinationFactorsTemperatureTimeAmount of catalyst1st80°C10 sec1 mg2nd80°C20 sec2 mg3rd85°C10 sec2 mg4th85°C20 sec1 mgTwo-level orthogonal arrays are used in this study. There areN factors, where N is the number of design factors (variables), and each factor has two levels. To establish an orthogonal array of N factors with two levels, let Ln(2n-1) denote n-1 columns and n individual experiments corresponding to the n rows, where L stands for Latin square, n=2k, k positive integer (k>1), and N≤n-1. More details about generating a two-level orthogonal array are available in [8, 9].After evaluation of then combinations, the summarized data are analyzed using factor analysis. Factor analysis can evaluate the effects of individual factors on the objective (or fitness) function, rank the most effective factors, and determine the better level for each factor such that the function is optimized. In this study, the factor analysis in [10] is used. Let yt denote a function value of the combination t, where t=1,…,n. Define the main effect of factor j with level k as Ejk, where j=1,…,N and k=1,2:(2)Ejk=∑t=1nyt⋅Ft,
where Ft=1 if the level of factor j of combination t is k; otherwise, Ft=0. Considering the case that the optimization objective is to be minimized, the level 1 of factor j makes a better contribution to the function than level 2 of factor j does when Ej1<Ej2. If Ej1>Ej2, level 2 is better. On the contrary, if the objective is to be maximized, level 1 (level 2) is better when Ej1>Ej2(Ej1<Ej2). If Ej1=Ej2, levels 1 and 2 have the same contribution. After the better one of two levels of each factor is determined, a reasoned combination consisting of N factors with better levels can be easily derived.


## 3. Orthogonal Genetic Algorithm

In this section, the orthogonal genetic algorithm is introduced.A crossover operator formed by the orthogonal array and the factor analysis is employed to enhance the performance of genetic algorithm. The application of the orthogonal experiment design and factor analysis in the crossover operator is firstly illustrated by a simple example. Here, we choose the test functionf(x)=∑i=17xi2, where xi∈{0,1}. The test function would be minimized.First, two individualsx′ and x′′, each consists of seven factors corresponding to xi of f(x), are randomly chosen to execute various matrix experiments of an orthogonal array. Regarded as level 1, the values of seven factors in individual x′ are 1, 1, 1, 1, 0, 0, and 0; those in individual x′′ are 0, 0, 0, 0, 1, 1, and 1, which are regarded as level 2. Therefore, f(x′)=4 and f(x′′)=3. The orthogonal array L8(27) has been chosen, because each individual has seven variables (factors). Then, seven variables in individuals x′ and x′′ corresponding to the factors A, B, C, D, E, F, and G, respectively, are defined in Table 3.Table 3
Corresponding factors ofx′ and x′′.
FactorsABCDEFGLevel 1(x′)1111000Level 1(x′′)0000111Next, as shown in Table4, the values of level 1 and level 2 are assigned to the level cells in the orthogonal array L8(27). The function values of each row (offspring individual) are then calculated. Then, formulation (2) is applied in Table 4 to determine the optimum level for each factor. The function value of an optimum individual found by OGA is zeros, which is much better than its parent individuals x′ and x′′ with f(x′)=4 and f(x′′)=3. It is obvious that instead of executing all 27 combinations of factor levels, the orthogonal design with factor analysis can offer an efficient approach toward finding the optimum individual by only executing eight experiments.Table 4
Generating a better individual from two individuals by orthogonal experiment design and factor analysis.
Combination number(j)FactorsFunction valueABCDEFG111110004211101116310010114410001002501010014601001102700110104800101012Ej116161616121212Ej212121212161616Optimum level2222111Optimum individual00000000The pseudocode of OGA is given as shown in Algorithm1.Algorithm 1:The pseudocode of OGA.
Initialize: population size NP, crossover rate Pcrossover, mutation rate Pmutation, maximal number ofgenerationsGmax and current generation counter gInitialize randomly the initial populationP0 of size NP binary stringsEvaluate each individualx∈P0WHILEstopping rule is not invoked DOSelect a suitable two-level orthogonal arrayLn(2n-1) for matrix experiments//Orthogonal Crossover OperationFORi=1 to NPIFrandom(0,1)<PcrossoverSelect two random parentsxa,xb∈PgExecute the matrix experiment forxa and xbCalculate the function values of individuals generated by the matrix experimentCalculate the effects of the various factors(Ej1,Ej2) by using factor analysis,Generate an optimal individualxc†END IFXc←xc†END FORNP_temp=size(Pg∪Xc)//Mutation OperationFORi=1 to NP_tempIFrandom(0,1)<PmutationSelect one random individualxm∈Pg∪Xc, and choose randomly two gene bits in this individualChange these two gens from 0 to 1 or vice versa, and generate a new individualxm†END IFXm←xm†END FOR//Select OperationSortPg∪Xc∪XmCopy theNP first solutions of Pg∪Xc∪Xm, and retain the best individualEND WHILE


## 4. Problem Formulation

Consider a planar thinned array consisting of2M×2N identical elements with half-wavelength spaced, which is symmetric about the x-axis and y-axis. Assume that the array elements are all isotropic. Radiation pattern of the array is calculated by the standard expression of the array factor:(3)F(W,u,v)=4∑m=1M∑n=1Nwmncos⁡⁡[π(2m-1)u]⋅cos⁡[π(2n-1)v],
where u=sinθcosφ and v=sinθsinφ are the direction parameters, and W={wmn,m=1,2,…,M;n=1,2,…,N}, wmn=1or0 if an element is “ON” or “OFF” at the location (m,n).The number of radiating elements in a thinned array depends on the array fill factor that represents the fraction of the turned ON elements in relation to the total number of elements and its value ranges between zero and one. The array fill factor is expressed as∑m=1M∑n=1NWmn/M×N. Therefore, the array thinning factor (i.e., the fraction of the turned OFF elements in relation to the total number of elements) can be written as follows:(4)Γ=1-1M×N∑m=1M∑n=1NWmn.The peak sidelobe level of the planar thinned array can be formulated as follows:(5)fplanar(W)=max⁡(u,v)∈S|F(W,u,v)FFmax⁡|,
where S denotes the sidelobe region and FFmax is the peak of the main beam.To suppress PSLL in allφ planes, the fitness function can be defined as(6)f(W)=fplanar(W).In [4], the fitness function is the sum of the maximum PSLL in the φ=0° and φ=90° planes. That is,(7)f(W)=fplanar(W)|φ=0°+fplanar(W)|φ=90°.According to [7], the fitness function is written by(8)f(W)=max⁡{fplanar(W)|φ=0°,fplanar(W)|φ=90°}.Hence, a planar thinned array synthesis can be modeled into the following optimization problem:(9)min⁡f(W)
is solved by means of OGA to find the optimal array geometry, that is, the optimal amplitude weights W={wmn,m=1,2,…,M;n=1,2,…,N}.


## 5. Numerical Results and Comparisons

In order to assess the capabilities of the OGA, comparisons with the GA [4], MGA [5], and ACO [7] in thinning 20×10-element planar arrays have been performed in this section. Parameter settings of the OGA are as follows. Throughout this paper, population size NP=50; crossover probability Pcrossover=0.3; mutation probability Pmutation=0.1; and the maximum number of generations is set to Gmax=50. Due to the randomness nature, the algorithm is run 20 times independently for each problem and the best results are presented in this section.In the first case, the planar thinned array was optimized by the GA [4] and MGA [5]. Equation (7) is selected as fitness function. The fitness value of the optimal solution by the GA [4] and MGA [5] was −39.83 dB and −45.456 dB, respectively. Here, the OGA is employed to optimize the planar thinned arrays. In order to maintain a fair comparison with published result [4, 5], the array with 54% filled is constrained and the aperture size of the array is 9.5λ×4.5λ. Figure 1 shows the radiation patterns and the corresponding geometry of the optimized array by OGA. The best fitness value is −51.18 dB (PSLL = −26.09 dB in φ=0° plane, and PSLL = −25.09 dB in φ=90° plane). As shown in Table 5, the optimal result by OGA is 11.35 dB and 5.724 dB lower than that of the best array in [4, 5], respectively. The optimal array by OGA has a directivity of 24.66 dB. In comparison, the array optimized by the GA has a directivity of 24.64 dB and the array optimized by the MGA has a directivity of 25.07 dB. Compared with the array by MGA, the directivity of the array by OGA is reduced by 0.59 dB. The convergence curve of the algorithm is plotted in Figure 2. Figure 3 reports the resultant fitness value of 20 runs. It can be seen from Figure 3 that, with the same maximum number of iterations, the fitness values obtained by OGA vary in the range from −47.93 dB to −51.18 dB. The worst-case fitness value found by OGA is also lower than that by the GA [4] and MGA [5].Table 5
Comparison with the GA [4] and MGA [5] for the planar array.
AlgorithmPeak PSLL(φ=0°)Peak PSLL(φ=90°)Best fitness valueDirectivityGA [4]−20.07 dB−19.76 dB−39.83 dB24.64 dBMGA [5]−29.597 dB−15.859 dB−45.456 dB25.07 dBOGA−26.09 dB−25.09 dB−51.18 dB24.66 dBFigure 1
Radiation patterns of the optimized 20×10-element planar array with the fitness function as (7).Figure 2
The convergence curve of the OGA for the planar array with the fitness function as (7).Figure 3
The fitness value of the objective function with (7) obtained by the OGA in 20 runs.Another optimization problem is the reduction of PSLLs in allφ planes. To maintain a fair comparison with published result in [5], the array with 50% filled is optimized by OGA. Equation (6) is acted as the fitness function. Figure 4 shows the three-dimension radiation pattern of the optimal array by OGA. Figure 5 is the radiation patterns in φ=0°, φ=45°, and φ=90° planes and the corresponding geometry of the optimal array by OGA. The corresponding array pattern in Figure 4 has a directivity of 24.82 dB with a maximum PSLL 19.44 dB below the main beam. In comparison with the results in [5], the peak PSLL is reduced by 0.6 dB.Figure 4
Three dimension radiation pattern of the optimized array for reduction PSLL in allφ planes.Figure 5
Radiation patterns inφ=0°, φ=45° and φ=90° planes.The second thinned planar array with published results is the same array geometry as before but with different fitness function. Equation (8) is selected as the fitness function. In order to maintain a fair comparison with published result [7], the thinning percentage of the array is not constrained. Figure 6 shows the radiation patterns and the corresponding geometry of the optimized array. The best OGA synthesized array yields PSLL = −28.34 dB in φ=0° plane, and PSLL = −26.59 dB in φ=90° plane, thus improving the PSLL of the best results (PSLL = −25.76 dB in φ=0° plane, and PSLL = −25.67 dB in φ=90° plane reported in [7]) in the literature of about 2.58 dB and 0.92 dB, respectively. The thinning percentage of the optimal array is 42%, which is better than 32% of the optimal array by the ACO [7]. The optimal array by OGA has a directivity of 25.44 dB. Compared with the array optimized by the ACO, the directivity of the optimal array is reduced by 0.55 dB. As shown in Table 6, it can be found that the results by OGA are better than that by the ACO [7] in terms of both the thinning percentage and PSLL. It can be seen from Figure 7 that the algorithm has very fast convergence speed (about 11th iteration). Moreover, Figure 8 reveals that out of 20 independent trials, there are 14 times for which OGA can find the best result, and even the worst result found by OGA is also equal to the best result of ACO [7].Table 6
Comparison with the ACO [7] for the planar array.
AlgorithmThinning percentage (%)Peak PSLL(φ=0°)Peak PSLL(φ=90°)DirectivityACO [7]32%−25.76 dB−25.67 dB25.99 dBOGA42%−28.34 dB−26.59 dB25.44 dBFigure 6
Radiation patterns of the optimized 20×10-element planar array with the fitness function as (8).Figure 7
The convergence curve of the OGA for the planar array with the fitness function as (8).Figure 8
The fitness value of the objective function with (8) obtained by the OGA in 20 runs.


## 6. Conclusion

In this paper, the orthogonal genetic algorithm is employed to design thinned array with a minimum PSLL. The method is a genetic algorithm based on orthogonal design. The performance of the OGA has been evaluated by synthesis 20×10-element planar arrays. The experiment results show that the OGA yields lower PSLL than the results reported in literature.



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