Population-based Optimization Algorithm Research Articles

Renewable Energy Sources constrained Generation Scheduling of Thermal Units in a Regulated Power Industry

Renewable energy production technologies use resources directly from the environment to generate electric power. These energy sources such as sunshine, wind, tides and biomass are some of the more popular gifts from the nature. The renewable energy resources emit nothing into atmosphere or never produce green house gas or pollute the environment at all. Even if the renewable energy sources are safe to the atmosphere, these resources meet out smaller percent of power requirement internationally This paper a population based optimization algorithm of Corona Virus Herd Immunity Optimizer (CVHIO) is proposed to solve the renewable energy constrained generation scheduling problem. The objective function of the problem is to minimize the total operating cost of hybrid system with satisfying standard operating constrains of thermal, wind and solar power plants. Initially the variables of the problem are effectively optimized using CVHIO algorithm. . Numerical example with IEE 39 bus test system (ten thermal twenty four hour) with an equivalent Solar and Wind generators are considered to prove the performance of CVHIO approach.

Differential Evolution with perturbation mechanism and covariance matrix based stagnation indicator for numerical optimization

Differential Evolution (DE), as a promising population-based stochastic optimization algorithm, has drawn attention from researchers of various fields owing to its simple operation, strong robustness and few control parameters. However, classic DE suffers from drawbacks such as premature convergence and stagnation resulted from reduced population diversity when tackling complicated optimization problems with high dimensions. To mitigate these deficiencies, Differential Evolution with Perturbation mechanism and Covariance Matrix based stagnation indicator (PCM-DE) is proposed in this paper. There are three main modifications in the algorithm. First, a two-phase parameter adaptation strategy based on fitness value is proposed to guide the search towards promising direction according to different stages of evolution. Second, a perturbation mechanism for archived population is proposed, where a new weight coefficient is defined by exploiting information of fitness value and position in archived individuals. Third, a stagnation indicator based on covariance matrix is proposed to assess the population diversity and the information of variance is employed to perturb stagnant individuals. To validate the effectiveness of PCM-DE, it is compared with several state-of-the-art DE variants and non-DE based algorithms under a large test suite containing 100 benchmark functions. Besides, three truss optimization problems are also employed to verify the feasibility and scalability of the algorithm. The experiment results confirm its highly competitive performance in terms of solution accuracy and convergence speed.

An adaptive differential evolution with opposition-learning based diversity enhancement

Differential Evolution (DE), as a powerful population-based stochastic optimization algorithm, has attracted the attention of researchers from various fields due to its advantages such as simple operation, strong robustness, and few control parameters. However, many existing DE variants often suffer from drawbacks such as premature convergence and stagnation when solving complicated optimization problems. In view of the aforementioned issues, this paper proposes an adaptive DE with opposition learning-based diversity enhancement (OLBADE). The main contributions can be summarized as follows: Firstly, a new adaptive parameter control is proposed with a non-linear weighting strategy incorporating into the framework of parameter adaptation. Secondly, a donor vector perturbation strategy is introduced to complement existing strategy for increasing population diversity. Thirdly, a novel stagnation indicator is proposed, and then opposition learning strategy is employed to renew stagnated individuals in the population when stagnation occurs. OLBADE is compared with five excellent DE variants under a large test-bed containing CEC2013, CEC2014, CEC2017 and CEC2022 test suites to verify its effectiveness. In addition, OLBADE is applied in parameter identification problem of photovoltaic model to verify its feasibility. Experimental results demonstrate that OLBADE achieves higher solution accuracy, faster convergence speed and better stability.

Design optimizer for planar soft-growing robot manipulators

Soft-growing robots are innovative devices that feature plant-inspired growth to navigate environments. Thanks to their embodied intelligence of adapting to their surroundings and the latest innovations in actuation and manufacturing, it is possible to employ them for specific manipulation tasks. The applications of these devices include exploration of delicate/dangerous environments, manipulation of items, or assistance in domestic environments.This work presents a novel approach for design optimization of soft-growing robots, which will be used prior to manufacturing to suggest to engineers – or robot designer enthusiasts – the optimal size of the robot to be built for solving a specific task. The design process is modeled as a multi-objective optimization problem, optimizing the kinematic chain of a soft manipulator to reach targets and avoid unnecessary overuse of material and resources. The method exploits the advantages of population-based optimization algorithms, in particular evolutionary algorithms, to transform the problem from multi-objective into single-objective thanks to an efficient mathematical formulation, the novel rank-partitioning algorithm, and obstacle avoidance integrated within the optimizer operators. The proposed method was tested on different tasks to assess its optimality, which showed significant performance in solving the problem: the retrieved designs are short, smooth, and precise at reaching targets. Finally, comparative experiments show that the proposed method works better than the one existing in the literature in terms of precision (14% higher), resource consumption (2% shorter configurations with 4% fewer links), actuation (85% less wavy and undulated configurations), and run time (13% faster).

An archive-based self-adaptive artificial electric field algorithm with orthogonal initialization for real-parameter optimization problems

In this article, a series of learning strategies are proposed to enhance the optimization ability of the artificial electric field algorithm. Orthogonal learning is an important mathematical tool that can greatly influence the adaptability of population-based optimization algorithms. This article proposes, (i) an orthogonal array-based learning strategy to generate a better initial population for the artificial electric field algorithm. Along with the changes in the initialization mechanism, this article also proposes, (ii) an archive-based self-adaptive learning strategy for an artificial electric field algorithm. The proposed learning strategy divides the population into ordinary and extraordinary sub-populations, each with distinct learning mechanisms. The ordinary sub-population utilizes six learning strategies based on three archives, which contain individuals of different quality levels. We incorporate, (iii) a mutation strategy also to update the extraordinary sub-population. Finally, (iv) a self-adaptive strategy is implemented to dynamically adjust the parameters of the proposed algorithm. The effectiveness of these mechanisms is assessed through an extensive analysis of exploration–exploitation dynamics and diversity. Furthermore, an independent structural study is conducted to examine the impact of implemented mechanisms on the algorithm’s behavior and efficiency. The proposed algorithm is evaluated on real parameter CEC 2017 problems across different dimensional search spaces. It is compared to eleven state-of-the-art algorithms, and the results demonstrate superior performance in terms of solution accuracy, convergence rate, search capability, and stability. The overall ranking highlights its exceptional potential for solving challenging optimization problems. Additionally, it outperforms other state-of-the-art algorithms across various dimensions, achieving accuracy rates of 64.48%, 70.05%, 78.73%, and 79.25% for dimensions 10, 30, 50, and 100, respectively. Furthermore, it demonstrates superior performance, outperforming others in 73.13% and 60.61% of the problems concerning average accuracy and statistical significance across all dimensions, respectively.

I-CPA: An Improved Carnivorous Plant Algorithm for Solar Photovoltaic Parameter Identification Problem.

The carnivorous plant algorithm (CPA), which was recently proposed for solving optimization problems, is a population-based optimization algorithm inspired by plants. In this study, the exploitation phase of the CPA was improved with the teaching factor strategy in order to achieve a balance between the exploration and exploitation capabilities of CPA, minimize getting stuck in local minima, and produce more stable results. The improved CPA is called the I-CPA. To test the performance of the proposed I-CPA, it was applied to CEC2017 functions. In addition, the proposed I-CPA was applied to the problem of identifying the optimum parameter values of various solar photovoltaic modules, which is one of the real-world optimization problems. According to the experimental results, the best value of the root mean square error (RMSE) ratio between the standard data and simulation data was obtained with the I-CPA method. The Friedman mean rank statistical analyses were also performed for both problems. As a result of the analyses, it was observed that the I-CPA produced statistically significant results compared to some classical and modern metaheuristics. Thus, it can be said that the proposed I-CPA achieves successful and competitive results in identifying the parameters of solar photovoltaic modules.

Uncovering structural bias in population-based optimization algorithms: A theoretical and simulation-based analysis of the Generalized Signature Test

Population-based optimization algorithms are popular tools for optimization, influenced by two interactive factors: landscape bias, which guides the population toward better objective function values, and structural bias, which directs the population to a specific location in the search domain. Structural bias can impede the exploration of algorithms, compelling the population to revisit a particular area of the search domain, which leads to increased computing costs without yielding any new information. In this study, we present a comprehensive survey of structural bias. Identifying this bias in a general algorithm is challenging. We propose the ‘Generalized Signature Test’ as a methodology to detect and analyze the structural bias in population-based optimization algorithms. Numerical simulations are performed using six well-known metaheuristic algorithms: Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, Grey Wolf Optimizer, Whale Optimization Algorithm, and Harris Hawks Optimization. Experimental results suggest that Differential Evolution displays the least bias toward the center of the search domain, whereas the Harris Hawks Optimization exhibits a significant axial-diagonal bias toward the origin. Our findings indicate that the proposed method is both efficient and straightforward for assessing algorithmic structural bias. The Generalized Signature Test provides an in-depth analysis and understanding of biases, tailor-made for thorough bias behavior investigations. The Generalized Signature Test can identify bias in established algorithms and evaluate bias in emerging algorithms at their early development stages.

Liquid Silicone Rubber Headlamp Lens Injection Molding Process Optimization Based on Tie Bar Elongation and NSGA III.

This study aimed to improve the injection molding quality of LSR material lenses by optimizing the process parameters. To achieve this goal, we employed the population-based optimization algorithm NSGA-III, which can simultaneously optimize multiple objective functions and identify an equilibrium point among them, thereby reducing the time required to find the optimal process parameters. We utilized analysis software to simulate the injection molding process of LSR material lenses, with a specific focus on examining the relationship between tie bar elongation and the optimized process parameters. During the study, we intentionally varied key process parameters, including the melt temperature, holding pressure, and holding time, to analyze their impact on the residual stress of the final product. In order to investigate the intricate relationship between the tie bar yield, injection molding process parameters, and lens residual stress, we installed strain sensors on the tie bar to continuously monitor changes in clamping force throughout the injection molding process. The experimental results showed that both the tie bar force and mold cavity pressure exerted significant influence on residual stresses. By applying the NSGA-III algorithm for optimization, we successfully determined the optimal process parameters, which included a melt temperature of 34.92 °C, a holding pressure of 33.97 MPa, and a holding time of 9.96 s. In comparison to the initially recommended process parameters during the design phase, the optimized parameters led to reductions of 12.98% in clamping force and 47.14% in residual stress. Furthermore, the average transmittance of the actual product remained within the range of 95-98%. In summary, this approach not only enables the prediction of the lens's residual stress trends based on the tie bar elongation, but also leads to a substantial enhancement of lens quality, characterized by reduced residual stress and improved transmittance through the optimization of process parameters. This methodology can serve as a valuable guide for optimizing real-world injection molding processes.

EGFA-NAS: a neural architecture search method based on explosion gravitation field algorithm

Neural architecture search (NAS) is an extremely complex optimization task. Recently, population-based optimization algorithms, such as evolutionary algorithm, have been adopted as search strategies for designing neural networks automatically. Various population-based NAS methods are promising in searching for high-performance neural architectures. The explosion gravitation field algorithm (EGFA) inspired by the formation process of planets is a novel population-based optimization algorithm with excellent global optimization capability and remarkable efficiency, compared with the classical population-based algorithms, such as GA and PSO. Thus, this paper attempts to develop a more efficient NAS method, called EGFA-NAS, by utilizing the work mechanisms of EGFA, which relaxes the search discrete space to a continuous one and then utilizes EGFA and gradient descent to optimize the weights of the candidate architectures in conjunction. To reduce the computational cost, a training strategy by utilizing the population mechanism of EGFA-NAS is proposed. In addition, a weight inheritance strategy for the new generated dust individuals is proposed during the explosion operation to improve performance and efficiency. The performance of EGFA-NAS is investigated in two typical micro search spaces: NAS-Bench-201 and DARTS, and compared with various kinds of state-of-the-art NAS competitors. The experimental results demonstrate that EGFA-NAS is able to match or outperform the state-of-the-art NAS methods on image classification tasks with remarkable efficiency improvement.

Global optimization and structural analysis of Coulomb and logarithmic potentials on the unit sphere using a population-based heuristic approach

Global optimization of high-dimensional non-convex functions arises in many important applications and researches. In this paper, we focus on the minimum energy configuration problem on the unit sphere, whose goal is to determine the minimum-energy configuration of N particles interacting via the standard Coulomb potential or the discrete logarithmic potential on the unit sphere. This is a classic global optimization problem with many important applications in mathematics, physics, biology and chemistry, and is one of the 18 great mathematical problems for the twenty-first century listed by the Fields Medal winner (Smale, 1998). To determine the minimum-energy configuration for these two potential functions, we propose a population-based global optimization algorithm that combines a two-phase local optimization method with early-stopping strategy, a random perturbation operator, a distance-based population update strategy using a novel distance function to measure the difference between solutions, and a population rebuilding method. Computational results show that the proposed algorithm is very competitive compared to the best known results in the literature. In particular, it improves the best-known result for one instance of the classic Thomson problem (i.e., N=360) widely studied in the literature. The configurations of instances with up to N=500 particles are systematically optimized for the first time by the proposed algorithm for the discrete logarithmic potential. The comparative study shows that the putative optimal configurations have a high similarity for the Coulomb and logarithmic potentials and that the defects play an important role in reducing the energy of configuration. The energies of particles on the putative optimal configurations are analyzed for the studied logarithmic potential.

Identification of parameters for equivalent circuit model of Li-ion battery cell with population based optimization algorithms

As EVs are sold to worldwide area with different latitudes, it also implies that lithium-ion batteries need to be safely operated in different climatic environments. To ensure the robustness and safeness of EVs, the characteristics of lithium-ion batteries must be taken into considerations. To comprehensively grasp the key information of battery cell in an efficient way, building a battery model is one of the most effective ways. Based on the model, one can get rid of time-consuming experiments. Moreover, the model enables to simulate and observe battery behaviors under extreme test condition safely. In this paper, an equivalent electric circuit (EEC) of a Li-ion battery cell is investigated. Based on 2nd order RC circuit, an Extended Hybrid Pulse Power Characterization (EHPPC) is designed and carried out to observe the dynamic response of battery cell in time domain. Once substantial measurement results are obtained, the parameterization turns out to be an important issue. This paper investigates the most appropriate metaheuristic based method to rapidly and systematically identify the EEC parameters. Totally four different methods, namely Particle Swarm Optimization, Grey Wolf Optimizer, Harmony Search, and Golden Eagle Optimization, are realized and equally compared by 100 implementations. Eventually, a mission profile of driving pattern, Dynamic Stress Testing (DST), is used to validate the proposed EEC model. The 2RC EEC model is able to simulate battery voltage response with root-mean-squared error as low as 0.0045.

Advanced slime mould algorithm incorporating differential evolution and Powell mechanism for engineering design

The slime mould algorithm (SMA) is a population-based swarm intelligence optimization algorithm that simulates the oscillatory foraging behavior of slime moulds. To overcome its drawbacks of slow convergence speed and premature convergence, this paper proposes an improved algorithm named PSMADE, which integrates the differential evolution algorithm (DE) and the Powell mechanism. PSMADE utilizes crossover and mutation operations of DE to enhance individual diversity and improve global search capability. Additionally, it incorporates the Powell mechanism with a taboo table to strengthen local search and facilitate convergence toward better solutions. The performance of PSMADE is evaluated by comparing it with 14 metaheuristic algorithms (MA) and 15 improved MAs on the CEC 2014 benchmarks, as well as solving four constrained real-world engineering problems. Experimental results demonstrate that PSMADE effectively compensates for the limitations of SMA and exhibits outstanding performance in solving various complex problems, showing potential as an effective problem-solving tool.

A fusion algorithm based on whale and grey wolf optimization algorithm for solving real-world optimization problems

In order to better understand and analyze population-based meta-heuristic optimization algorithms, this paper proposed a new hybrid algorithm combined Lévy flight with modified Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) , which is called LMWOAGWO to discard the dross and select the essence. Firstly, the population is initialized by using the uniform distribution space combined with the pseudo-reverse learning strategy, which lays the foundation for global search. Then, modifications were made to both WOA and GWO. For WOA algorithm, random adjustment control parameters strategy and different chaotic maps are used to adjust the main parameters of WOA to avoid the algorithm falling into local optimum in the later stage. For GWO algorithm, a new optimal solution is added to the grey wolf population to increase the optimal update position of the algorithm. On this basis, the dynamic weighting strategy is introduced to improve the convergence accuracy and convergence speed of the algorithm. Subsequently, new conditions were added during the WOA exploitation phase to formulate LMWOAGWO and the greedy strategy is used to retain better iteration update locations. Finally, Lévy flight is used to improve the global search ability of the algorithm. Extensive numerical experiments were conducted using 23 standard test benchmark functions, 25 CEC2005 functions, 15 popular benchmark functions and 10 CEC2019 functions to test the performance of LMWOAGWO compared with other well-known swarm optimization algorithms. Experimental and statistical results show that the performance of LMWOAGWO algorithm is better than many state-of-the-art algorithms. Then, 22 real-world optimization problems were used to further study the effectiveness of LMWOAGWO. Winners of CEC2020 Real World Single Objective Constraint Optimization Competition, such as iLSHADEϵ algorithm, sCMAgES algorithm, COLSHADE algorithm and EnMODE algorithm are selected as four comparison algorithms in real world optimization problems. Experimental results show that the proposed LMWOAGWO has the capability to solve real-world optimization problems. Finally, the application efficiency of LMWOAGWO in solving two basic optimization problems in wireless networks is briefly introduced, and compared with the original WOA and GWO. Simulation results show that the performance of the LMWOAGWO is better than WOA and GWO.

Towards Robust Task Assignment in Mobile Crowdsensing Systems

Mobile Crowdsensing (MCS), which assigns outsourced sensing tasks to volunteer workers, has become an appealing paradigm to collaboratively collect data from surrounding environments. However, during actual task implementation, various unpredictable disruptions are usually inevitable, which might cause a task execution failure and thus impair the benefit of MCS systems. Practically, via reactively shifting the pre-determined assignment scheme in real time, it is usually impossible to develop reassignment schemes without a sacrifice of the system performance. Against this background, we turn to an alternative solution, i.e., proactively creating a robust task assignment scheme offline. In this work, we provide the first attempt to investigate an important and realistic RoBust Task Assignment (RBTA) problem in MCS systems, and try to strengthen the assignment scheme's robustness while minimizing the workers' traveling detour cost simultaneously. By leveraging the workers' spatiotemporal mobility, we propose an assignment-graph-based approach. Firstly, an assignment graph is constructed to locally model the assignment relationship between the released MCS tasks and available workers. And then, under the framework of evolutionary multi-tasking, we devise a population-based optimization algorithm, namely EMTRA, to effectively achieve adequate Pareto-optimal schemes. Comprehensive experiments on two real-world datasets clearly validate the effectiveness and applicability of our proposed approach.

Boosted local dimensional mutation and all-dimensional neighborhood slime mould algorithm for feature selection

The slime mould algorithm (SMA) is a population-based optimization algorithm that mimics the foraging behavior of slime moulds with a simple structure and few hyperparameters. However, SMA has some limitations, such as getting trapped in local optima when dealing with multimodal or combinatorial functions. To overcome these limitations and improve the algorithm's exploration and exploitation abilities, a local dimensional mutation strategy and an all-dimensional neighborhood search strategy for SMA, known as LASMA, were introduced. To evaluate the performance of LASMA, experiments were conducted on 30 benchmark functions from the CEC2014 competition, and the results were compared with up to 27 peers. The experimental results were then synthesized, and the Wilcoxon signed-rank test was used to evaluate the performance of LASMA. The results showed that LASMA outperformed other algorithms in terms of solution accuracy, stability, and convergence speed, with at least a 53.3% improvement in optimization performance on the 30 tested functions. Moreover, to demonstrate the applicability of LASMA to feature selection problems, a binary version of LASMA called bLASMA was developed and compared with eight binary classification algorithms on 18 datasets from the UCI repository. The experimental results showed that bLASMA not only had faster convergence speed and higher convergence accuracy in handling optimization problems but also performed well in feature selection applications. Thus, LASMA is a promising optimization tool for handling global and binary optimization problems, and its binary version, bLASMA, can be used for feature selection tasks. By addressing the limitations of SMA and improving the algorithm's exploration and exploitation abilities, LASMA provides a robust and effective solution for various optimization problems.

Hybridized random forest with population-based optimization for predicting shear properties of rock fractures

Rock mass fractures play an essential role in controlling the shear behavior of natural rocks. The clear understanding of the fracture's shear parameters is of great significance in maintaining underground structure stability. A novel approach combining the random forest (RF) model and two population-based optimization algorithms respectively (the Sine Cosine Algorithm (SCA) and the whale optimization algorithm (WOA)) was proposed to predict the shear strength, peak shear displacement, and dilation angles of rock fractures. 84 direct shear data was divided into training set and test set for training and testing the proposed models. Coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE), three indexes were utilized to evaluate the model prediction accuracy. The prediction performance indicated that SCA-RF and WOA-RF models achieved high accuracy in predicting the shear strength and dilation angles of rock fractures. These two models also perform satisfactorily in predicting the peak shear displacement by optimizing the dataset division and fitness function. Comparing with Barton-Bandis and Asadollahi-Tonon empirical models, the proposed models have significant advantages in estimating the shear properties of rock fractures. It has important implications for the development of accurate and reliable models for predicting the shear behavior of natural rocks, that some data processing techniques were used based on the characteristics of the direct shear dataset.

Differential Evolution with wavelet basis function based parameter control and dimensional interchange for diversity enhancement

Differential Evolution (DE) is a potent population-based global optimization algorithm which has already proven efficient for optimization demands in engineering applications. However, even the state-of-the-art DE variants still suffer premature convergence and lack of diversity. In order to overcome the above mentioned weaknesses, this paper proposes a brand-new DE variant, namely zDE algorithm, for single-objective numerical optimization. The main contributions can be summarized as follows: First, an improved wavelet basis function is incorporated into the generation of the scale factor F and a Minkowski Distance based adaptation scheme is proposed for the adaptation of it. Second, a new trial vector generation strategy is first proposed as a supplementary of the existing strategies, and t-distribution is incorporated into this strategy. Third, a novel diversity enhancement technique is firstly proposed by changing the dimensional parameters of the individuals in the population. The zDE algorithm is validated under 88 benchmarks from the CEC2013, CEC2014, and CEC2017 test suites for real-parameter single-objective optimization, and the results show the superiority of our algorithm in comparison with the recent state-of-the-art DE variants.

A solar irradiance estimation technique via curve fitting based on dual-mode Jaya optimization

Solar irradiance is a crucial environmental parameter for optimal control of photovoltaic (PV) systems. However, precise measurements of the solar irradiance are difficult since the irradiation sensors (i.e., pyranometer or pyrheliometer) are expensive and hard to calibrate. This paper proposes a cost-effective and accurate method for estimating the solar irradiance with a PV module via curve fitting. A dual-mode Jaya (DM-Jaya) optimization algorithm is introduced to extract the real-time value of solar irradiance from the measured PV characteristics data by using two search strategies. The step sizes of a random walk are taken from even and Lévy distribution distributions in different searching phases. Compared with the traditional irradiance sensors, the proposed estimator does not require additional circuit and obtains relatively lower error rates. A comparative study of seven population-based optimization algorithms for the optimal design of the estimator is presented. These algorithms include particle swarm optimization (PSO), cuckoo search (CS), Jaya, simulated annealing (SA), genetic algorithm (GA), supply-demand-based optimization (SDO), and the proposed DM-Jaya algorithm. Simulations and experimental results reveal that DM-Jaya outperforms the other optimization algorithms in terms of the estimation speed and accuracy.

Approaches for BIM-based multi-objective optimization in construction scheduling

Construction scheduling is a complex process due to the interdependence and contradiction of project activities. This requires applying population-based optimization algorithms like evolutionary algorithms to reach optimal solutions. However, when optimizing more than three objectives, the efficiency of such algorithms degrades and trade-offs among conflicting objectives must be made to obtain an optimal Pareto Frontier. Recently, there have been attempts to integrate Building Information Modelling (BIM) with Multi-Objective Optimization (MOO) algorithms to solve building design and management problems like construction. This paper aims to assess the potential of developing models that combine information systems and optimization methods for better decision-making process. A structured literature review is provided giving insights on the predominant multi-objective optimization approaches and the active BIM-based optimization models, with an emphasis on the potential of providing more effective and robust construction process that combine BIM with optimization tools. Finally, gaps are addressed, and recommendations are proposed for future research development.

Backtracking search algorithm with dynamic population for energy consumption problem of a UAV-assisted IoT data collection system

In recent years, collecting data from IoT devices by unmanned aerial vehicles (UAVs) has become a very hot research topic. This paper focuses on the energy consumption problem of a UAV-based IoT data collection system. To solve the considered energy consumption problem, this paper proposes a new population-based optimization algorithm called the backtracking search algorithm with dynamic population (BSADP), which can determine the optimal number and locations of stop points of the UAV simultaneously. In addition, BSADP has a simple framework, which consists of the proposed enhanced backtracking search algorithm (EBSA) and the designed population adjustment mechanism with opposition-based learning (PAMOBL). In the search process, the population is regarded as the entire deployment of the UAV. BSADP firstly generates the trail deployment of the UAV by EBSA and then the next generation deployment of the UAV is produced based on the trail deployment and PAMOBL. The performance of BSADP is investigated by two energy consumption formulations. Experimental results support the superiority of BSADP in optimizing the deployment of the UAV and prove the application value of BSADP in the real scenario. The source code of the proposed algorithm can be found from: https://github.com/jsuzyy/BSADP.