# Application of Cuckoo Search Algorithm in Cost Estimation of Building Energy Engineering

Authors: Liangqiong Chen
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

In order to solve the problem that the construction project cost estimation is at a relatively advanced stage, many information about the project cannot be determined, and many unforeseen things affecting the project cost will occur in the process of project construction, which makes the preparation of project investment estimation very difficult. An optimized firefly algorithm is proposed. By introducing cuckoo algorithm, the initial population of fireflies is optimized, which greatly improves the quality of the initial population and speeds up the convergence of fireflies to the optimal solution; Secondly, the performance of CSFA algorithm is tested by six standard test functions; Finally, the algorithm is applied to solve the pressure vessel design problem. The effectiveness of the experiment is verified.


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## Body

## 1. Introduction

In the early stage of the construction project, the project cost is also the investment estimation. In this paper, the main function of the model is to predict the cost of the residential project and make a reference for the investment estimation. Therefore, the estimation mentioned in this paper is the investment estimation in the early stage of the construction project. In the stage of project initiation and feasibility study of traditional construction projects, most of the project cost of the proposed project is estimated by estimation indicators. The so-called project cost estimation index refers to the economic price of the construction and installation project producing a certain unit of measurement (such as m2, m3 or building, seat) and the consumption standard of labor, materials, and construction machines and tools. The preparation process is to select a representative, in line with the technical development direction, sufficient and reusable design drawings, and project budget and settlement data of their quantities, which are comprehensively determined after classification, screening, and statistical analysis. The investment estimation data and engineering material consumption of the proposed project can be roughly obtained by using the project cost estimation index. In addition to using the traditional investment estimation index to calculate the investment estimation of the construction project, quantitative exponential smoothing method, regression analysis method, and qualitative brainstorming method can also be used. Or use fuzzy mathematics, genetic algorithm, and other theories to model and solve. The investment estimation of construction projects can not only provide a reference basis for selecting technologically advanced and economically reasonable architectural design schemes, make the design of construction drawings more reasonable, and have an irreplaceable impact on the early investment decision-making. It also affects the design budget estimate in the subsequent stage and the cost management and control in the implementation of the project. Cuckoo algorithm of project cost is a fast quotation method of project cost [1]. It is a method of applying the basic principle of fuzzy mathematics to quantitatively compare and study the similar procedures of the structural schemes of the proposed project and the built project of the same structural system, so as to quickly estimate the cost of the proposed project by using the cost data of similar built projects, as shown in Figure 1. The greatest advantage of cuckoo algorithm is that when the proposed project is still in the “hazy” stage, that is, when the preliminary design drawings are incomplete, or when the construction drawings are relatively complete, the project cost can be estimated quickly and accurately without calculating the quantities of divisional and subdivisional works and applying the budget quota [2]. The traditional cost calculation method is generally the project budget based on the budget quota when the design drawings are relatively complete. This traditional calculation method has long calculation cycle and slow speed, and it is difficult to meet the requirements of construction market competition after the implementation of bidding. Even if the computer budget preparation software is used, a large number of quantities calculation still need to be completed manually, resulting in the complexity and time-consuming of cost calculation. The cuckoo algorithm of project cost can quickly quote the project cost, which provides a fast and accurate calculation method for the bidding quotation and base price determination of bidding and contracting projects [3].Figure 1
Flow chart of building energy engineering cost estimation.


## 2. Literature Review

Ren, K. and others found that in recent years, with the adjustment of relevant national policies, the development speed of China’s construction industry tends to be stable. The economic benefits generated by the construction industry are still the main part of China’s national economy [4]. Fang, Y. and others believe that, for example, in 2017, the GDP of construction industry accounted for 27% of China’s GDP with 18 trillion yuan, which is an important part of China’s GDP [5]. Wang, R. Q. and others found that with the slowdown of the national economic development trend, the real estate industry has also changed the past rapid growth trend and entered the stage of gentle development and industry consolidation [6]. Nugraha, D. A. and others believe that at this stage, the market demand is lower than that in previous years, and the profit margin is also reduced accordingly. The idea of trying to reduce costs to maximize profits has gradually become the mainstream of the long-term development of major real estate enterprises [7]. Zhao, M. and others think that China’s project cost management started relatively late, paid insufficient attention to it in the construction industry, and the establishment of theoretical knowledge system is not perfect. Some traditional cost methods have been gradually eliminated. Instead, it is necessary to estimate the early stage of the project and take preventive measures, namely, pre-control [8]. Wang, H. and others found that there are two main engineering cost estimation methods used in China at this stage [9]: One is that the cost engineer uses the incomplete original data and previous experience of the project, estimates the consumption of main engineering materials of the proposed project according to the unified quota of the country, industry and region, the actual price of materials, and the cost quota, and calculates the engineering cost in combination with the actual situation of the project. The other is to estimate the cost by using experience and skills according to the technical and economic indicators and actual cost data of similar projects. Gosain, A. and others found that the construction project itself has the characteristics of complexity and long construction cycle, which determines that the construction cost of the construction project is also independent, unique, and complex [10]. However, the factors affecting the project cost are not single, which has great limitations on the personal experience of manufacturing engineers in cost estimation, and cannot meet the accuracy requirements of the project. Therefore, Wang, W. and others found that practitioners urgently need to establish a fast, accurate estimation method that meets the accuracy requirements of project cost at this stage both in the process of design scheme and optimization calculation [11]. Liu, J. and others believe that in recent years, in the era of rapid development of computers, high-precision cost estimation through mathematical modeling method is more and more respected by practitioners [12]. Wu, Z. and others use computer modeling. First, they need to input a large amount of data, establish a database, and then establish a mathematical model. On this basis, they mainly rely on human judgment, and finally deal with the problem in combination with the mathematical model. Cuckoo algorithm is a popular mathematical model, which has strong application value in project cost estimation [13].


## 3. Method

According to the scope involved, there are two definitions of project cost. In a broad sense, project cost refers to all the investment expenses of fixed assets required for the construction of a project. It includes the cost of construction and installation engineering, the purchase cost of equipment and tools, other costs of engineering construction, and reserve funds. The composition of the project cost is shown in Figure2 [14].Figure 2
Project cost components.In a narrow sense, the project cost, which is often referred to as the project price, includes the expenses for obtaining the land and necessary equipment for the implementation of the project in order to complete the construction project, as well as the expenses for obtaining technology and labor services in the construction process. These expenses constitute the price of construction and installation project and the total price of construction project. From the perspective of participating units, for the investor or project legal person, the project cost is the project investment cost. For both parties, the project cost is the contract price of the project. In terms of coverage, the extension of project investment cost is multi-angle and multi-directional, including all costs in the process of project construction [2]. The project price is not comprehensive. Even for the “turnkey” project, the project price only includes the expenses in the project construction, and the management fees and other expenses of the construction unit are not considered. Therefore, the project investment cost will generally be greater than the project price.The basic content of project cost management is to reasonably determine the project cost and effectively control the project cost. Figure3 illustrates the contents of the whole project cost in detail.Figure 3
Whole process cost of construction project.The reasonable determination of the project cost is to reasonably determine the investment estimation, estimated cost, revised estimated cost, budgeted cost, contract price, settlement price, and final settlement price at each stage of the project construction process. The investment estimation is the first stage, and its importance is particularly important.3.1 In the stage of project proposal, the construction unit shall carry out preliminary work and cost control of long-term plan as the real estate developer of the proposed project after review, verification, and approval of relevant departments according to relevant national standards and preliminary design documents.3.2 In the feasibility study stage of the project, the investment estimate of the construction project shall be prepared in accordance with the provisions of relevant provisions. After being reviewed, verified, and approved by relevant departments, it shall be used as the control cost of the construction project. The purpose of cost control in this stage is to control the estimated price within the control cost of the project proposal.3.3 In the preliminary design stage, the general estimate of the preliminary design of the construction project shall be prepared in accordance with the provisions of relevant provisions. After being reviewed, verified, and approved by relevant departments, it will be used as the maximum limit of the project cost of the proposed project. The purpose of cost control in this stage is to control the estimated price within the estimated price.3.4 In the construction drawing design stage, the construction drawing budget shall be prepared according to the pre-designed construction drawings and relevant regulations. The main purpose is to verify whether the budgeted cost (construction drawing design stage) exceeds the approved preliminary design estimate (preliminary design stage).3.5 For the bidding process based on the construction drawing budget, the construction and installation project cost is taken as the contract price. The purpose of cost control at this stage is to control the construction contract price within the construction drawing budget price, which is used by the construction unit to determine the standard to measure the efficiency [15].3.6 During the project implementation, the settlement price is based on the contract price in the bidding process. It is determined according to the quantities actually completed by the contractor, the cost changes caused by price factors such as material price difference, the engineering costs actually occurred in the construction and unpredictable in the design, and the cost changes caused by force majeure.3.7 In the stage of completion acceptance, it is necessary to make a comprehensive and detailed summary of all expenses spent in the process of project construction, prepare the final account of completion, and truthfully record the actual cost incurred in the process of project construction. It runs through the whole process of project implementation from the bidding quotation of the project to the completion and settlement of the project [16]. Construction cost control is to take corresponding management measures, including organizational measures, economic measures, control the cost within the planned scope, and further seek the maximum cost savings on the premise of ensuring that the construction period and quality meet the requirements.Based on the optimization of the construction scheme and design scheme, effective measures and methods shall be taken to control the construction project cost within a reasonable range according to each stage of the construction process, and the approved cost limit shall not be exceeded at the same time. That is, the estimated cost in the preliminary design stage shall be controlled based on the estimated investment price, the revised estimated cost in the technical design stage shall be controlled based on the estimated cost, and the budgeted cost in the construction drawing design stage shall be controlled based on the revised estimated cost, so as to finally achieve the rational utilization of human, material, and financial resources and achieve better investment benefits [17].Firefly algorithm and engineering application is a cuckoo initialization algorithm. The firefly algorithm is inspired by the flashing behavior of fireflies. The main idea is to use the firefly with higher brightness to attract the firefly with lower brightness, and complete the position update in the process of moving from the firefly with lower brightness to the firefly with higher brightness. The basic mathematical model of firefly algorithm is shown in formulas (1)–(4):
(1)Ii=fxi,(2)βijrij=β0e−γr2ij,(3)rij=xi−xj=∑k=1dxi,k−xj,k2,(4)xjt+1=xjt+βijrijxit−xjt+αξj,where Ii is the absolute brightness of the i-th firefly; fxi is the objective function value; β0 is the most attractive; rij is the Cartesian distance from the i-th firefly to the j-th firefly; α is a constant; ξ is the random number vector obtained from Gaussian distribution, uniform distribution, or other distributions [18].Cuckoo algorithm is a random process that simulates cuckoo’s nest seeking and spawning flight. The algorithm can use the following three idealized conditions: (1) each cuckoo produces only one egg at a time and randomly in a nest; (2) the best quality bird’s nest will be reserved for the next generation; (3) fixed the number of nestsn, the probability that the nest host will find cuckoo eggs is pa∈. In this case, the nest owner can discard the bird’s eggs, or abandon the nest and re-establish a nest in a new place. As we all know, the initial value is of great significance to the heuristic algorithm. The selection of the initial population can directly affect the performance and convergence speed of the algorithm. In order to better improve the quality of the initial population of firefly algorithm, the idea of CS algorithm is applied to the position initialization process of FA algorithm, and a cuckoo initialized firefly algorithm is proposed, which improves the optimization performance of firefly algorithm.CSFA algorithm steps are as follows:(1)
Initialize the cuckoo population, set the number of nestsn, the maximum number of iterations n, and the discovery probability is p. Upper and lower bounds of search domain Ub, Lb(2)
Each nest is tested by using the objective function, and the current best solution is recorded to keep the optimal nest position to the next generation(3)
Use equation 5 to update other bird’s nest positions, compare the existing bird’s nest with the previous generation’s nest position, and if it is better, take it as the best position at present(4)
Using a random number that obeys uniform distribution and the probabilityp of cuckoo’s egg being found by the nest host. For comparison, if r>P, the position of the bird’s nest is randomly changed to obtain a new set of bird’s nest positions, and vice versa. Then, test the new location and keep the optimal location to the next generation(5)
Judge whether the end conditions are met. If not, return to step 2 and run again; if satisfied, it will jump out of the loop and output the optimal position(6)
Taking the optimal position obtained by the cuckoo algorithm as the initial position of the firefly algorithm, the fluorescence brightness of each firefly individual is calculated(7)
Using equations (1) and (2) to calculate the relative brightness and attraction between individuals, and determine the moving direction of individuals according to the relative brightness(8)
Update the individual position according to equation (3), randomly disturb the individual in the optimal position, calculate the fitness function value of each individual, and find the optimal solution(9)
Check whether the termination conditions are met [19–21]. If satisfied, the global optimal value is output; if the termination conditions are not met, return to step 7


## 4. Experiment and Discussion

The numerical experiment runs in Windows 7 environment and uses MATLAB 7 0 for programming. For all test functions, the basic parameter values set by CS algorithm are as follows: population sizen =25, the maximum number of iterations is 500, and the discovery probability is pα=0.25. The basic parameters set by FA algorithm are as follows: population size n =50, light intensity absorption coefficient λ=1, step factor α=0.02, maximum attraction β0=1, and maximum number of iterations of 500. The dimension, iteration times, and search space of benchmark function are shown in Table 1 [22, 23].Table 1
Dimension, iteration times, and search space of benchmark function.
Function nameDimensionMaximum number of iterationsSearch spaceTheoretical optimal valueSphere30500[-5.15,5.12]0Rastrigin30500[-5.15,5.12]0Rosenbrock10500[-30,30]0Ackley15500[-35,35]0Griew ank10500[-100,100]0Zakharov15500[-5,10]0In order to better verify the performance of CSFA algorithm, for the selected test function, FA algorithm, CS algorithm, and CSFA algorithm are used to run independently for 30 times. Among them, the worst value and the best value reflect the quality of understanding, the average value reflects the overall level of understanding, and the standard deviation reflects the stability of the algorithm. No matter from the optimal value, the worst value, or the standard deviation and average value, CSFA algorithm is significantly higher than FA algorithm and CS algorithm in optimization accuracy [24, 25]. In order to intuitively compare the optimization accuracy and convergence speed of the three algorithms, the iterative curves of FA algorithm, CS algorithm, and CSFA algorithm on six test functions are drawn, as shown in Figure 4. It can be found that CSFA algorithm can converge to the optimal solution faster than FA algorithm and CS algorithm, and the solution accuracy is greatly improved.Figure 4
Iterative curves on test functions.
(a)
Sphere function(b)
Rastrigin function(c)
Rosenbrock function(d)
Chung Reynolds function(e)
Girewank function(f)
Zakharov functionWith the development of heuristic algorithms, there are more and more new algorithms. In order to verify the performance of the new algorithms, they are used in various engineering structure design, and the most widely used is the pressure vessel design problem. It has four design variables: hemispherical thickness, thickness, internal radius, and length. The main goal is to minimize the total design cost under nonlinear constraints. CSFA algorithm is used to solve the pressure vessel design problem independently for 10 times, and the results are compared with those of SBSM algorithm, CPSO algorithm, HPSO algorithm, TVDFPA algorithm, HPSO algorithm, and TVDFPA algorithm. CSFA algorithm is better than other algorithms in solving pressure vessel problems in terms of optimal value, worst value, average value, and standard deviation [3, 26].


## 5. Conclusion

As a good algorithm, firefly algorithm has great potential in solving engineering optimization problems. In this paper, a cuckoo initialized firefly algorithm (CSFA) is proposed. Through the simulation experiment of six standard test functions, compared with the test results of the existing heuristic algorithm, the optimal solution with higher accuracy is obtained. In terms of application, CSFA algorithm is used in pressure vessel design, which also reflects better optimization performance. The pressure vessel design problem is a single objective and continuous optimization problem. In order to verify the universality of the algorithm, applying it to multi-objective and discrete optimization problems will be a research direction worthy of attention in the future.



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*Source: 7956751-2022-04-26.xml*