Global Search Phase Research Articles

An Improved Cuckoo Search Algorithm and Its Application in Robot Path Planning

This manuscript introduces an improved Cuckoo Search (CS) algorithm, known as BASCS, designed to address the inherent limitations of CS, including insufficient search space coverage, premature convergence, low search accuracy, and slow search speed. The proposed improvements encompass four main areas: the integration of tent chaotic mapping and random migration in population initialization to reduce the impact of random errors, the guidance of Levy flight by the directional determination strategy of the Beetle Antennae Search (BAS) algorithm during the global search phase to improve search accuracy and convergence speed, the adoption of the Sine Cosine Algorithm for local exploitation in later iterations to enhance local optimization and accuracy, and the adaptive adjustment of the step-size factor and elimination probability throughout the iterative process to convergence. The performance of BASCS is validated through ablation experiments on 10 benchmark functions, comparative experiments with the original CS and its four variants, and application to a robot path planning problem. The results demonstrate that BASCS achieves higher convergence accuracy and exhibits faster convergence speed and superior practical applicability compared to other algorithms.

Optimal deployment of private 5G multi-access edge computing systems at smart factories: Using hybrid crow search algorithm

In smart factories, an increasing number of mobile intelligent devices are deployed to meet the growing demands for flexible manufacturing. These devices, equipped with various sensors, synchronize a substantial amount of data with cloud servers for real-time monitoring and control. Fifth generation mobile networks (5G) combined with multi-access edge computing (MEC) provide the capabilities for multiple connections, large-scale data transmission, and efficient response times, becoming the mainstream network architectures for the needs. Additionally, with the emerging trend of energy-saving, recent works have addressed energy consumption as a significant cost factor. However, most previous studies tended to focus solely on cloud servers, neglecting energy consumption at edge computing and underestimating the impact of heterogeneous mobile device. As the scope of smart factory networks continues to expand, the challenges become increasingly critical. This study presents a private 5G MEC system architecture tailored for smart panel factories. The concerned problem is formulated as an integer programming model, which determines deployment locations and quantities for MEC servers and 5G small cells, including picocells and femtocells, so as to minimize the overall deployment costs while meeting the constraints of connectivity, capacity, latency, coverage, serviceability, and energy consumption. Since the edge server deployment problem is NP-hard, this study further proposes a hybrid metaheuristic algorithm, CSAVNS, which combines the strengths of crow search algorithm (CSA) and variable neighborhood search (VNS). In the global search phase of CSA, the VNS local search is introduced to enhance the algorithm's capabilities. Experimental analysis demonstrates that the proposed CSAVNS outperforms other algorithms in terms of solution solving capabilities.

A hierarchical surrogate assisted optimization algorithm using teaching-learning-based optimization and differential evolution for high-dimensional expensive problems

Surrogate-assisted evolutionary algorithms (SAEAs) are increasingly used in solving computationally expensive optimization problems. However, when tackling high-dimensional expensive problems, a large number of exact function evaluations (FEs) need to be consumed for existing SAEAs to achieve an acceptable solution. In this paper, a hierarchical surrogate assisted optimization algorithm (HSAOA) using teaching-learning-based optimization and differential evolution is proposed for solving high-dimensional expensive problems with a relatively small number of exact FEs. To keep a balance between global exploration and local exploitation, a hierarchical surrogate framework with hybrid evolutionary algorithms is devised. In the global search phase, a radial basis function surrogate is utilized to assist the teaching-learning-based optimization in locating the promising sub-regions. In the local search phase, a novel dynamic ensemble of surrogates is proposed to assist the differential evolution in speeding up the convergence process. Eight test functions with 10 to 100 dimensions and a spatial truss design problem are employed to compare the proposed method with several state-of-the-art SAEAs. The results show that the proposed HSAOA is superior to the comparison algorithms for solving expensive optimization problems, and needs a much smaller number of exact FEs than other competing SAEAs to produce competitive or even better results for high-dimensional expensive problems.

AI-based energy-efficient path planning of multiple logistics UAVs in intelligent transportation systems

In the context of Industry 5.0, artificial intelligence (AI)-based logistics Unmanned Aerial Vehicles (UAVs) have been widely applied in intelligent transportation systems due to their advantages of faster speed, lower cost, more environment-friendly, and less manpower needed. Whereas, most of the existing logistics UAV delivery models have not taken the energy consumption of the logistics UAVs and mixed time windows of the customers, which leads to their models cannot be applied in practical transportation systems. Therefore, we propose to minimize the total energy cost of multiple logistics UAVs during the customized products delivery period for a smart transportation system. Taking the energy consumption variation of the logistics UAVs, mixed time windows of the customers, as well as simultaneous delivery and pick up into consideration, we formulate a cooperative path planning problem via jointly optimizing the route of the logistics UAVs and the service allocation. To solve this large-scale integer programming problem, we employ the Large Neighborhood Search Algorithm (LNS) to accelerate the convergence rate of Genetic Algorithm (GA), and then develop an improved GA based cooperative path planning algorithm (IGCPA). The optimization procedure of the proposed algorithm IGCPA is divided into two phases, using the GA crossover operator and variational operator in the global search phase and LNS operator in the local search phase, and validating the integer programming model and the effectiveness of the solution algorithm based on different scale cases. Finally, abundant simulation results show that the energy cost of IGCPA is reduced by 17.35%, 15.18% and 9.99% compared with GA, LNS and Particle Swarm Optimization (PSO), respectively. Furthermore, the IGCPA is validated using Solomn standard data, which further verifies that the IGCPA can enhance the convergence rate of GA as well as obtain a lower delivery cost. Sensitivity analysis of the maximum UAV load and battery capacity reveals that the distribution cost tends to decrease and then increase as the increase of maximum load and battery capacity.

A novel hybrid Harris hawk optimization and sine cosine algorithm based home energy management system for residential buildings

Smart grid technology has given users the ability to regulate their home energy in a much more effective manner. In such scenarios, Home Energy Management (HEM) potentially becomes an arduous task, as it necessitates the optimal scheduling of smart appliances in order to reduce energy usage. In this research, a hybrid Harris Hawk Optimization-Sine Cosine Algorithm (hHHO-SCA) has been proposed to develop a meta-heuristic-based HEM system. The hybridization of HHO with SCA has been done to enhance the exploration and exploitation stages of HHO, hence improving its global search phase and effectively optimizing the energy usages. In addition to this, several knapsacks are utilized to guarantee that load demand for power users does not surpass a certain level during the peak hours. In terms of electricity prices and Peak to Average Ratio (PAR) reduction, the hybridization is demonstrated to be beneficial in achieving the objectives. Simulations are performed for a multi-family housing complex with a range of smart equipment. The results achieved with the proposed approach suggest that hHHO-SCA has been comparatively efficient in terms of cost reduction, and PAR, when compared to the other optimization techniques. Practical Application. This home energy management system can be applied to optimally schedule all the smart appliances in a building to minimize electricity consumption and provide the consumer with potential savings in electricity costs.

A MOEA/D with global and local cooperative optimization for complicated bi-objective optimization problems

Multi-objective evolutionary algorithm based on decomposition has been well-recognized in addressing bi-objective optimization problems. However, it is a serious challenge for MOEA/D to optimize real-world complicated bi-objective optimization problems which have irregular optimal objective spaces, such as long-tailed, peaked, and disconnected types. Hence, an improved MOEA/D with global and local cooperative mechanisms is proposed in this paper for complicated bi-objective optimization problems. The overall optimization is carried out by the coordination of global and local search phases. Specifically, a bidirectional global search strategy based on reference points is first used to broaden the search regions. Aiming at the irregularity of the Pareto fronts, an adaptive neighborhood search strategy is proposed according to the individual sparsity, which can further explore near the obtained global optimal solutions. The cooperation of these two strategies is designed to improve the distribution and uniformity of the population on complicated bi-objective optimization problems. The performance of the proposed algorithm is investigated on a representative set of benchmark test functions and a real-world water resource allocation problem. The simulation results show that the proposed algorithm is very promising for complicated bi-objective optimization problems.

An elitist cuckoo search algorithm for combined heat and power economic dispatch

Combined heat and power economic dispatch (CHPED) is one of the foremost subjects in the operation of power systems. In this article, a new variant of cuckoo search (CS) algorithm, elitist CS (yECS), is advanced to tackle CHPED. During the optimisation process of the original CS as well as lots of its variants, the guidance of search directions relies merely upon the best individual, causing the loss of other beneficial information and further influencing their performance potentials. Therefore, in yECS, an elitist mechanism is developed to fully utilise the beneficial information of other elite individuals. Specifically, three new iterative strategies are developed, one for the global search phase and the other two for the local search phase. Further, a coordinated mechanism is put forward to effectively integrate the local iterative strategies, thus helping yECS in maintaining an appropriate balance between exploitation and exploration. The superior performance of yECS is firstly substantiated via CEC 2017 test suite and two engineering design problems and then it is utilised to address CHPED problems. All optimal dispatch results acquired by yECS are feasible and in most cases display remarkable improvements over the results determined by other CS variants and some recently-published literature results.

An adaptive balance optimization algorithm and its engineering application

The policy of balance between exploration capability and exploitation capability directly affects the solution performance of the meta-heuristic algorithm in a limited time. In order to better balance the exploration and exploitation capabilities of the algorithm and meet the solution requirements of complex real-world problems, the adaptive balance optimization algorithm (ABOA) is proposed in this paper. The algorithm consists of a global search phase (GSP) and a local search phase (LSP) and is controlled by a fixed parameter. ABOA not only considers the balance of exploration and exploitation capabilities of the algorithm throughout the whole iterative process but also focuses on the balance of exploration and exploitation in both GSP and LSP. The search in both phases is focused around the respective search centers from outside to inside. ABOA balances the exploration and exploitation capabilities of the algorithm throughout the search process by two adaptive policies: changing the search area and changing the search center. Fifty-two unconstrained benchmark test functions were employed to evaluate the performance of ABOA. The results of ABOA were compared with nine excellent optimization algorithms available in the literature. The statistical results and Friedman test showed that ABOA was significantly competitive. Finally, the results of the examined engineering design problems showed that ABOA can solve the constrained optimization problem better compared to other methods.

Application of Improved Butterfly Optimization Algorithm Combined with Black Widow Optimization in Feature Selection of Network Intrusion Detection

Feature selection is a very important direction for network intrusion detection. However, current feature selection technology of network intrusion detection has the problems of low detection rate and low accuracy due to feature redundancy. An improved Butterfly Optimization Algorithm combined with Black Widow Optimization (BWO-BOA) is proposed in this paper, which introduces a dynamic adaptive search strategy in the global search phase of the Butterfly Optimization Algorithm (BOA), uses the movement search process of Black Widow Optimization (BWO) algorithm as the local search, and at the same time, in order to overcome the improved butterfly optimization algorithm easily falling into a local optimum in local search phase, takes advantage of the small probability mutation strategy to filter out the redundant features. This paper then tries to apply the proposed BWO-BOA algorithm to feature selection of network intrusion detection. In order to verify the performance of the proposed BWO-BOA algorithm, the UNSW-NB15 dataset is selected for binary classification and multi-classification simulation experiments, and the feature selection models of BWO-BOA algorithm, BOA algorithm, BWO algorithm, Particle Swarm Optimization, Salp Swarm Algorithm, Whale Optimization Algorithm and improved Butterfly Optimization Algorithm are compared for validation. The experimental results show that the proposed BWO-BOA algorithm can enhance the performance of the feature selection model in network intrusion detection and significantly boost the reduction of feature dimensions.

Chaos-Enhanced Adaptive Hybrid Butterfly Particle Swarm Optimization Algorithm for Passive Target Localization.

This paper considers the problem of finding the position of a passive target using noisy time difference of arrival (TDOA) measurements, obtained from multiple transmitters and a single receiver. The maximum likelihood (ML) estimator’s objective function is extremely nonlinear and non-convex, making it impossible to use traditional optimization techniques. In this regard, this paper proposes the chaos-enhanced adaptive hybrid butterfly particle swarm optimization algorithm, named CAHBPSO, as the hybridization of butterfly optimization (BOA) and particle swarm optimization (PSO) algorithms, to estimate passive target position. In the proposed algorithm, an adaptive strategy is employed to update the sensory fragrance of BOA algorithm, and chaos theory is incorporated into the inertia weight of PSO algorithm. Furthermore, an adaptive switch probability is employed to combine global and local search phases of BOA with the PSO algorithm. Additionally, the semidefinite programming is employed to convert the considered problem into a convex one. The statistical comparison on CEC2014 benchmark problems shows that the proposed algorithm provides a better performance compared to well-known algorithms. The CAHBPSO method surpasses the BOA, PSO and semidefinite programming (SDP) algorithms for a broad spectrum of noise, according to simulation findings, and achieves the Cramer–Rao lower bound (CRLB).

A ranking-based fuzzy adaptive hybrid crow search algorithm for combined heat and power economic dispatch

This paper attempts to obtain optimal generation scheduling for Combined Heat and Power Economic Dispatch (CHPED) problems and seeking a possible solution for the global optimization of test systems. As such, a Fuzzy adaptive Ranking- based Crow Search Algorithm (FRCSA) is amalgamated with modified Artificial Bee Colony (ABC). The proposed algorithm (FRCSA-ABC) has been integrated with three mechanisms in order to achieve competitive results effectively. The first mechanism uses the Fuzzy logic Inference System (FIS) to tune dynamically the parameters of flight length (fl) and awareness probability (CAP) of each crow. The second mechanism emerges from a natural phenomenon known as proximate optimality principle (POP) which reveals that better individuals will often have beneficial information and more probabilities to select and guide other individuals. This observation leads to the feasibility-based Ranking Crow Search Algorithm (RCSA) which generates a new food source from the chosen higher rankings of parent food sources. The third mechanism integrates the two modified global search phases of ABC with a local search phase of FRCSA to ensure promising performance. During the evaluation process, this mechanism investigates the aging level of the individual’s best solution (pbest) in order to choose an appropriate search phase in FRCSA and two modified phases of ABC. The performance of the algorithm is tested on four CHPED test systems, 23 well-known benchmark functions, and test suit of CEC2017. The results obtained are compared with its variants as well as with existing different approaches to validate its optimal search efficiency. Further, non-parametric statistical tests such as pair-wise and multiple comparisons tests are adapted to establish the supremacy of the FRCSA-ABC. The seminal aspect of this intended algorithm is that it can achieve cost-effectiveness conforming to meticulous global convergence.

Muscle-Effort-Minimization-Inspired Kinematic Redundancy Resolution for Replicating Natural Posture of Human Arm.

Replicating natural postures of human arms is essential to generate human-like behaviors in robotic applications for humans nearby. However, how to realize this requirement in interactive scenarios remains a challenge due to the kinematic redundancy and unknown postural control strategy of human arms. Inspired by the physiological characteristics that the musculoskeletal system is coordinated to minimize muscle effort in human behaviors, this paper aims to address the issue by solving a muscle effort minimization problem. It adopts a high-fidelity human arm musculoskeletal model (HAMM) and considers the implicit constraint (desired hand pose) and the inequality constraints (range of joint motion). The constrained minimization is in general nonconvex, consequently sensitive to initial guesses in iterative procedures. So, it is impracticable to solve it directly with existing gradient-based deterministic approaches or standard evolutionary algorithms. As the main contribution, a hybrid inverse kinematics algorithm was proposed for the HAMM with 7 independent and 13 mimic joints to obtain the feasible arm postures satisfying the minimization constraints. Using the arm swivel angle that parametrizes the kinematic redundancy of the HAMM, geometrically equidistant initial guess candidates can be generated over the 1-dimension feasible posture manifold. As another contribution, we present a two-phase global minimization algorithm to handle the nonconvexity of the constrained minimization. It consists of a local-search phase on the null-space of the geometric Jacobian matrix and a global-search phase with an initial guess resampling strategy. The proposed approach was validated by replicating the natural arm postures of 5 right-handed subjects in daily tasks.

Combined heat and power economic dispatch using an adaptive cuckoo search with differential evolution mutation

In power system operation, the combined heat and power economic dispatch (CHPED) is an attractive and momentous optimization problem where the major objective is to find an optimal generation schedule of heat and power to meet the heat and power demands with the minimum cost, while satisfying various practical operation constraints. This paper puts forward an adaptive cuckoo search with differential evolution mutation (ACS-DEM) for solving the CHPED problem. Compared with the basic cuckoo search (CS), there are three main improvements in the proposed ACS-DEM. The first improvement is that adaptive parameters are employed and therefore no parameter adjustment is required. The second is the incorporation of a Gaussian sampling strategy into the global search phase of the algorithm to increase the exploration capability. The third is the introduction of an improved differential evolution mutation strategy into the local search phase to replace the simple biased random walk in the basic CS, thus discouraging the blindness and enhancing the exploitation capability. The outstanding performance of ACS-DEM is first confirmed through the test suite from the 2017 Conference on Evolutionary Computation and then demonstrated on several CHPED problems. The obtained dispatch schedules from ACS-DEM are feasible and in most cases exhibit a distinct improvement over the results offered by six other CS-based algorithms, one state-of-the-art differential evolution algorithm, as well as recent works in this field.

COVID-19 X-ray image segmentation by modified whale optimization algorithm with population reduction

Coronavirus disease 2019 (COVID-19) has caused a massive disaster in every human life field, including health, education, economics, and tourism, over the last year and a half. Rapid interpretation of COVID-19 patients' X-ray images is critical for diagnosis and, consequently, treatment of the disease. The major goal of this research is to develop a computational tool that can quickly and accurately determine the severity of an illness using COVID-19 chest X-ray pictures and improve the degree of diagnosis using a modified whale optimization method (WOA). To improve the WOA, a random initialization of the population is integrated during the global search phase. The parameters, coefficient vector (A) and constant value (b), are changed so that the algorithm can explore in the early stages while also exploiting the search space extensively in the latter stages. The efficiency of the proposed modified whale optimization algorithm with population reduction (mWOAPR) method is assessed by using it to segment six benchmark images using multilevel thresholding approach and Kapur's entropy-based fitness function calculated from the 2D histogram of greyscale images. By gathering three distinct COVID-19 chest X-ray images, the projected algorithm (mWOAPR) is utilized to segment the COVID-19 chest X-ray images. In both benchmark pictures and COVID-19 chest X-ray images, comparisons of the evaluated findings with basic and modified forms of metaheuristic algorithms supported the suggested mWOAPR's improved performance.

Multi-population and dynamic-iterative cuckoo search algorithm for linear antenna array synthesis

Nature inspired algorithms have served as the backbone of modern computing technology and over the past three decades, the field has grown enormously. CS is a recent addition to optimization computing and suffers from problem of local optima stagnation and poor exploration. This paper presents a new version of CS namely extended CS (ECS) by employing the concept of multi-population adaptation, adaptive switching, dynamic iterative search, and GWO inspired global search phase, for solving benchmark problems and real-world design problem of linear antenna array (LAA) optimization. The algorithm aims at providing a better framework for optimization problems by keeping the original structure of CS intact. For performance evaluation, the algorithm has been firstly applied on two highly challenging datasets namely CEC 2015 and CEC 2017 benchmark problems and performance evaluation is done in comparison with other algorithms. Further to test the ECS algorithm on real world application, it is applied for synthesis of uniform and non-uniform LAA. Experimentally, the ECS algorithm is found to provide better performance in comparison to basic CS and others state-of-the-art algorithms. Statistical tests and radiation patterns further validate the results.

An enhanced whale optimization algorithm for large scale optimization problems

Whale optimization algorithm was developed based on the prey-catching characteristics of the humpback whales. Due to its simple structure and efficiency, the researchers employed the algorithm to address numerous disciplines’ numerous problems. The profound analysis of the whale optimization algorithm discloses that the algorithm suffers from low exploration ability, lesser accuracy, and early convergence. Additionally, performance of the whale optimization algorithm and most of its variants in high-dimensional optimization problems is not satisfactory. This study proposes a new variant with several modifications to the basic whale optimization algorithm to solve high-dimensional problems. A unique selection parameter is introduced in the whale optimization algorithm to balance the algorithm’s global and local search phase. The co-efficient vectors A and C are modified and used effectively to explore and exploit the search region better. In the exploration phase, random movement is allowed to reduce the computational burden of the algorithm. An inertia weight is introduced in the exploitation phase for exhaustive search nearby the best solution. The proposed algorithm evaluates twenty-five benchmark functions using dimensions 100, 500, 1000, and 2000 and compared the results with the whale optimization algorithm and its variants. The estimated outcomes are also compared with seven basic metaheuristic algorithms. Finally, statistical analysis, complexity analysis, and convergence analysis are performed to establish the algorithm’s efficacy. All the test result suggests better performance of the proposed algorithm on higher-dimensional problems.

Improved interval methods for solving circle packing problems in the unit square

In this work computer-assisted optimality proofs are given for the problems of finding the densest packings of 31, 32, and 33 non-overlapping equal circles in a square. In a study of 2005, a fully interval arithmetic based global optimization method was introduced for the problem class, solving the cases 28, 29, 30. Until now, these were the largest problem instances solved on a computer. Using the techniques of that paper, the estimated solution time for the next three cases would have been 3–6 CPU months. In the present paper this former method is improved in both its local and global search phases. We discuss a new interval-based polygon representation of the core local method for eliminating suboptimal regions, which has a simpler implementation, easier proof of correctness, and faster behaviour than the former one. Furthermore, a modified strategy is presented for the global phase of the search, including improved symmetry filtering and tile pattern matching. With the new method the cases n=31,32,33 have been solved in 26, 61, and 13 CPU hours, giving high precision enclosures for all global optimizers and the optimum value. After eliminating the hardware and compiler improvements since the former study, the new proof technique became roughly about 40–100 times faster than the previous one. In addition, the new implementation is suitable for solving the next few circle packing instances with similar computational effort.

Reverse guidance butterfly optimization algorithm integrated with information cross-sharing

Aiming at the disadvantages of slow convergence and the premature phenomenon of the butterfly optimization algorithm (BOA), this paper proposes a modified BOA (MBOA) called reverse guidance butterfly optimization algorithm integrated with information cross-sharing. First, the quasi-opposition concept is employed in the global search phase that lacks local exploitation capabilities to broaden the search space. Second, the neighborhood search weight factor is added in the local search stage to balance exploration and exploitation. Finally, the information cross-sharing mechanism is introduced to enhance the ability of the algorithm to jump out of the local optima. The proposed MBOA is tested in fourteen benchmark functions and three constrained engineering problems. The series of experimental results indicate that MBOA shows better performance in terms of convergence speed, convergence accuracy, stability as well as robustness.

New enhanced colliding body optimization algorithm based on a novel strategy for exploration

Researchers use the improvement strategies to enhance the current version of optimization algorithms to establish the right balance between local and global search phases to escape from a local minimum in some NP-hard problems. Colliding Bodies Optimization (CBO) and its enhanced version (ECBO) use the concept of mass and energy conservation law based on Newtonian Classic Physics. The quality of mentioned algorithm performance can be improved by changing how the bodies collide with each other. New enhanced colliding bodies Optimization (NECBO) moves all bodies towards the best solution in consecutive iterations to increase the chance of accessing a better solution and exploring all promised solution space. In this paper, the recently developed meta-heuristic algorithm, so-called Enhanced Colliding Bodies Optimization (ECBO), and one new proposed version of ECBO (called NECBO), including a new mechanism for finding new solutions, and two hybrid versions of both methods mentioned above (ECBO, NECBO) are utilized for the optimal design of sloped-roof rigid frames with non-prismatic members. The steel sloped portal frames, or steel gable frames, are frequently used in industrial buildings, gyms, schools, colleges, fire stations, storages, hangars , and many other low-rise structures. The gable frames' weight and shape are considerably affected by the member cross-sections' properties. The optimization of steel portal frames with tapered web members is a hard NP problem because of discrete variables, multiple local optimum, and corresponding constraints. The obtained results show the capability of NECBO and its hybridized version with ECBO in find a better solution than the conventional ECBO and CBO. • New enhanced colliding bodies’ optimization (NECBO) is developed by altering how bodies colide each other. • Enhanced Colliding Bodies Optimization (ECBO) and the new proposed NECBO include a new mechanism for finding new solutions. • The hybrid version of both ECBO and NECBO are utilized for optimal design of gable frames with tapered members. • Optimization of gable rigid frames with tapered web members is performed. • Results obtained from this study show the capability of NECBO and its hybridized version with ECBO.

Quantum Teaching-Learning-Based Optimization algorithm for sizing optimization of skeletal structures with discrete variables

In terms of optimization algorithm improvement, researchers utilize some strategies to establish the right balance between local and global search phases to escape from a local minimum in some NP-hard problems. The approach of quantum-behaved particles, as a novel strategy, uses uncertainty law and a distinct formulation obtained from solving the time-independent Schrodinger differential equation in the delta-potential-well function to update the solution candidates' positions. New formulation defines the local attractors as probable solutions between the best solution and the others to explore all solution space domain. Also, mentioned formulation employs the difference between the average solution and other ones as a new step size. Both local attractors and new step sizes guarantee diversification besides the inherent intensification. In the present paper, the teacher phase of the Teaching-Learning-Based Optimization algorithm (TLBO), which has been introduced recently, is improved using the formulation obtained from solving the time-independent Schrodinger equation to predict the probable positions of optimal solutions. The results show that QTLBO, an acronym for the Quantum Teaching- Learning- Based Optimization, improves the stability and robustness of the TLBO by defining the quantum teacher phase. The two planner rigid frames and two trusses with strength and serviceability constraints are chosen to verify the quality and performance of QTLBO. Comparing the results obtained from the proposed algorithm with those of the standard version of the TLBO algorithm and other literature methods shows that QTLBO increases the chance of finding a better solution besides improving the statistical criteria compared to the current TLBO.