Teaching Learning Based Optimization Research Articles

Optimum sizing of stand-alone microgrids: Wind turbine, solar photovoltaic, and energy storage system

The growing energy demand and rising fossil fuel expenses in isolated and remote regions have increased interest in renewable energy sources (RESs). However, RESs such as photovoltaics (PVs) and wind turbines (WTs) are intermittent and fluctuating; hence reliability is a major concern. The hybridization of RESs with energy storage systems can effectively address their fluctuations and intermittency and enhance overall efficiency; however, the corresponding system cost is a primary concern. Thus, determining the optimal sizing of a hybrid system is the major challenge. Previous studies have suggested metaheuristic algorithms that rely on specific parameters to find an optimal solution. To address this issue, a hybrid algorithm, namely JGWO, which combines JAYA and grey wolf optimizer (GWO), has been proposed. The aim is to solve the unit sizing problem of a hybrid system (PV-WT-battery) that can meet consumers’ demands at the lowest total annual cost (TAC) while considering the maximum allowable probability of power supply loss (LPSPmax) to ensure system reliability. The JGWO is compared to JAYA, GWO, genetic optimization algorithm (GOA), and teaching-learning based optimization (TLBO) in reliability and TAC. Findings reveal that JGWO outperforms existing algorithms at LPSPmax values of 0%, 1%, 3%, 2%, and 5%, yielding TAC of 66542, 61102, 50247, 43046, and 34464 USD, respectively, for a hybrid system (PV-WT-battery). The results also show that the proposed hybrid PV-WT-battery system is more cost-efficient than PV-battery and WT-battery systems, regardless of the LPSPmax value.

RETRACTED: Distributed IoT services placement in fog environment using optimization-based evolutionary approaches

Fog computing is a promising computing paradigm for the processing and storage of massive data generated by Internet of Things (IoT) devices. Fog computing devices have limited resources compared to the cloud. Meanwhile, the dynamism and heterogeneity of applications requested from IoT devices require quick decisions on where to process services on the fog, the cloud, or the hybrid. This problem is known as Fog Service Placement (FSP), which is a computationally NP-hard problem. Therefore, the efficient allocation of fog resources to IoT services by non-deterministic algorithms is emphasized. In this paper, a Quality of Service (QoS)-aware deployment scheme is proposed using an evolutionary approach to address the FSP problem. Here, several new evolutionary approaches such as Teaching Learning-Based Optimization (TLBO), Honey Badger Algorithm (HBA) and Harris Hawks Optimizer (HHO) are analyzed to solve the problem. We improve the evolutionary approach used to solve the FSP problem with a shared parallel architecture and perform distributed fog resource management. In addition, we bring the processing closer to the fog network by considering the priority of service execution. Here, we formulate a multi-objective function as an optimization problem that simultaneously considers delay cost, delay violation, service cost and fog utilization. All the parameters needed to configure the objective function are collected based on the incoming traffic. The simulations show the superiority of HBA for solving the FSP problem. The evaluation results show that the proposed placement scheme improves the cost and delay compared to the best existing method by 2.6% and 2.8%, respectively.

Portfolio selection model using teaching learning-based optimization approach

<p>Portfolio selection is among the most challenging processes that have recently increased the interest of professionals in the area. The goal of mean-variance portfolio selection is to maximize expected return with minimizing risk. The Markowitz model was employed to solve the linear portfolio selection problem. However, due to numerous constraints and complexities, the problem is so critical that traditional models are insufficient to provide efficient solutions. Teaching learning-based optimization (TLBO) is a powerful population-based nature-inspired approach to solve optimization problems. This article presents a portfolio selection model using the TLBO approach to maximize the portfolio's Sharpe Ratio. The Sharpe ratio combines both expected return and risk. This algorithm model the natural teaching process of the classroom with two main phases, viz., teaching and learning. Performance analysis has been undertaken to investigate the suitability of TLBO based solution approach by comparing it with genetic algorithm (GA) and particle swarm optimization (PSO) on the real datasets, Deutscher Aktienindex (DAX) 100, Hang Seng 31, Standard and Poor’s (S&P) 100, financial times stock exchange (FTSE) 100, and Nikkei 225. The empirical results verify the superiority of the TLBO over GA and PSO.</p>

Modeling and optimization of conflicting responses in the laser transmission welding process using RSM, PSO, and TLBO algorithm

Laser transmission welding (LTW) is a widely used polymer welding technology in industries today. The performance of LTW is governed by a number of process parameters, and fine-tuning those parameters is critical for the process to achieve the intended results. Optimization of process parameters for enhancing LTW performance, like weld strength and weld width, is always tricky since they are inherently contradictory. Therefore, in this research, an effort is made to optimize the LTW parameters utilizing the three best-known optimization approaches, and the performance of those optimization approaches in LTW process optimization is compared. First, the response surface method (RSM) is employed to build empirical equations that establish an empirical correlation between process variables and desired performance features. These empirical equations are then employed as objective functions for process optimization utilizing the RSM-based desirability function approach (DFA), particle swarm optimization (PSO), and teaching learning-based optimization (TLBO) algorithms. The performance of the chosen optimization approaches is compared with reference to optimum results, accuracy, convergence rate, and computing time. PSO and TLBO algorithms outperform the DFA approach for single and multi-objective optimizations. TLBO is found to have faster convergence, whereas PSO takes less time for computation.

An integrated hybrid metaheuristic model for the constrained scheduling problem

Several problems in the domains of project management (PM) and operations research (OP) can be classified as optimization problems which are classically NP-Hard. One such highly important problem is the resource constrained project scheduling problem (RCPSP). The main aim of this problem is to find a schedule of the lowest and optimum makespan to complete a project, which involves resource as well as precedence constraints. But, being classically NP-Hard, the RCPSP requires exponential computational resources as the problem complexity increases. Thus, approximate techniques like computational intelligence (CI) based approaches provide better chances of finding near optimal solutions. This paper presents the usage of a hybrid technique using the phases of teaching learning-based optimization (TLBO) metaheuristic integrated with operators like crossover and mutation from the genetic algorithm (GA). An integrated hybrid using TLBO and 2-point crossover is applied in the teacher and learner phases to the discrete RCPSP problem. Further, to diversify the population, and enhance global search, the mutation operator is applied. The proposed model is extensively tested on well-known benchmark test instances and has been compared with other seminal works. The encouraging results make evident the efficiency of the provided solution for the RCPSP problem of varying magnitudes.

SCHEISB: Design of a high efficiency IoMT security model based on sharded chains using bio-inspired optimizations

Securing Internet-of-Medical Things (IoMT) devices is a multidomain task that involves analysis of attack patterns from data & control packets. Existing security models either work on pre-emptive analysis, real-time analysis or post-processing analysis of different packet types. These operations add to the complexity of data processing, which reduces Quality of Service (QoS) levels of IoMT deployments. To overcome these issues, this paper proposes design of a sharded chain based high efficiency IoMT security model that uses bioinspired optimizations. The proposed model initially deploys a novel single chained Proof-of-Medical-Trust (PoMT) based consensus technique, which assists in identification of high-trust miners. The QoS levels are continuously monitored for each block request, and a Teacher–Learner based Optimization (TLbO) model is deployed to make sharding decisions. These decisions are reconfirmed by a Particle Swarm Optimization (PSO) model, which assists in estimation of shard length under different attack types. These attacks include Spoofing, Spying, and Flooding attacks, which affects the authenticity of data samples from IoT devices under real-time scenarios. The PSO Model also recommends splitting & merging decisions based on the context of IoMT deployments. The PoMT consensus is modified to support sharded chains, and assists in reducing mining delays without compromising miner trust levels. The modified PoMT consensus is capable of selecting overlapping miner nodes, while incorporating load balancing for optimal energy consumption during the mining process. Due to these enhancements, the proposed model is able to reduce mining delay by 8.5%, while reducing energy consumption by 3.2%, and reducing access delay by 4.9% under real-time clinical scenarios.

Predicting steady-state biogas production from waste using advanced machine learning-metaheuristic approaches

This research offers a fast and accurate method for measuring the biogas production rate throughout biogas production. An agricultural biogas plant's measurement of eight process variables served as the source of experimental data used to create the models. Biomass type, reactor/feeding, volatile solids, pH, organic load rate, hydraulic retention time, temperature, and reactor volume were utilized in this context. Artificial neural networks (ANN) were developed to evaluate the biogas production rate. The variable selection was carried out using the cuckoo optimization algorithm (COA), multi-verse optimization algorithm (MVO), leagues championship algorithm (LCA), evaporation-rate water cycle algorithm (ERWCA), stochastic fractal search (SFS), and teaching–learning-based optimization (TLBO). In this study, the model's size decreased, the important process variables were highlighted, and the ANN models' potential was enhanced for prediction. The proposed COA, MVO, LCA, ERWCA, SFS, and TLBO and ensembles are the outcome of using the abovementioned approaches to synthesize the multi-layer perceptron (MLP). To evaluate the effectiveness of the used models, we have developed a scoring system in addition to employing mean absolute error, mean square error, and coefficient of determination as accuracy criteria. Implementing the COA, MVO, LCA, ERWCA, SFS, and TLBO algorithms enhances the accuracy of the MLP. It is found that some of the used hybrid techniques could provide better prediction outputs than traditional MLP rankings. Additional investigation indicated that the ERWCA is better than the three other algorithms. The biogas production rate was estimated with the greatest precision with R2 = 0.9314 and 0.9302, RMSE of 0.1969 and 0.24925, and MAE of 0.1307 and 0.19591.

Optimal power flow using a hybridization algorithm of arithmetic optimization and aquila optimizer

In this paper, a hybridization method based on Arithmetic optimization algorithm (AOA) and Aquila optimizer (AO) solver namely, the AO-AOA is applied to solve the Optimal Power Flow (OPF) problem to independently optimize generation fuel cost, power loss, emission, voltage deviation, and L index. The proposed AO-AOA algorithm follows two strategies to find a better optimal solution. The first strategy is to introduce an energy parameter (E) to balance the transition between the individuals’ procedure of exploration and exploitation in AO-AOA swarms. Next, a piecewise linear map is employed to reduce the energy parameter’s (E) randomness. To evaluate the performance of the proposed AO-AOA algorithm, it is tested on two well-known power systems i.e., IEEE 30-bus test network, and IEEE 118-bus test system. Moreover, to validate the effectiveness of the proposed (AO-AOA), it is compared with a famous optimization technique as a competitor i.e., Teaching-learning-based optimization (TLBO), and recently published works on solving OPF problems. Furthermore, a robustness analysis was executed to determine the reliability of the AO-AOA solver. The obtained result confirms that not only the AO-AOA is efficient in optimization with significant convergence speed, but also denotes the dominance and potential of the AO-AOA in comparison with other works.

Versatility Investigation of Grown Titanium Dioxide Nanoparticles and Their Comparative Charge Storage for Memristor Devices.

Memristive devices have garnered significant attention in the field of electronics over the past few decades. The reason behind this immense interest lies in the ubiquitous nature of memristive dynamics within nanoscale devices, offering the potential for revolutionary applications. These applications span from energy-efficient memories to the development of physical neural networks and neuromorphic computing platforms. In this research article, the angle toppling technique (ATT) was employed to fabricate titanium dioxide (TiO2) nanoparticles with an estimated size of around 10 nm. The nanoparticles were deposited onto a 50 nm SiOx thin film (TF), which was situated on an n-type Si substrate. Subsequently, the samples underwent annealing processes at temperatures of 550 °C and 950 °C. The structural studies of the sample were done by field emission gun-scanning electron microscope (FEG-SEM) (JEOL, JSM-7600F). The as-fabricated sample exhibited noticeable clusters of nanoparticles, which were less prominent in the samples annealed at 550 °C and 950 °C. The element composition revealed the presence of titanium (Ti), oxygen (O2), and silicon (Si) from the substrate within the samples. X-ray diffraction (XRD) analysis revealed that the as-fabricated sample predominantly consisted of the rutile phase. The comparative studies of charge storage and endurance measurements of as-deposited, 550 °C, and 950 °C annealed devices were carried out, where as-grown device showed promising responses towards brain computing applications. Furthermore, the teaching-learning-based optimization (TLBO) technique was used to conduct further comparisons of results.

Analysis of drilling behavior of flax/PP composites

ABSTRACT The current research investigation aims to use and compare three different population-based optimization approaches to optimize the operating factors of drilling process to reduce thrust force (TF) and subsequently delamination in flax/PP laminates. Five different tool point geometries and three levels of spindle speed (SS) and feed rate (FR) were considered. Analysis of variance indicated that drill point geometry was most significant parameter affecting the TF, whereas SS had the most negligible impact. Moreover, the lowest and maximum TF and delamination factor (DF) were recorded for the U-shape drill bit and 135°- point angle twist drill bit, respectively. The outcome of the experimental investigation revealed that a low FR value (0.05 mm/rev) and medium-range SS value (1500–1800 RPM) were optimum parameters for each drill geometry. Although all optimization techniques provided almost the same optimum values, but TLBO (teaching learning-based optimization) technique’s convergence rate was fast compared to others.

Performance evaluation and extraction of global power maxima under shading scenarios and load Variation: An experimental study

Due to environmental degradation, depletion of fossil resources and contaminants in the air, energy structure reform and development of sustainable environment friendly are approaching in the past few years, in which several renewable energy technologies such as solar, wind, bio and hydropower as efficient alternative energy have drawn considerable attention. Solar Photovoltaic (SPV) systems, in particular, have huge benefits for society and the economy as an essential means of extracting and utilization. However, these systems suffer from extracting maximum power from variable irradiance due to shading scenarios and variable load. As a result of PSCs, many LMPPs and GMPPs exist on their power voltage (P-V) characteristics. Thus, MPTT techniques are implemented in these systems to resolve these issues. Several researchers have conducted substantial research and discussion on these techniques to improve SPV system performances, but the practical applicability of MPPT approaches faces multiple obstacles. This study evaluates the performances of three such MPPT metaheuristic approaches, i.e., umbrella optimization technique (UOT), particle swarm optimization (PSO) and teaching learning-based optimization (TLBO) experimentally in real-time environment on small hardware under PSCs and load variation scenarios. Each technique is tested on a standalone SPV system incorporating an Inverse Single-Ended Primary Inductor Converter (SEPIC) and Luo DC-DC converter separately on various parameters of SPV systems such as tracking time, tracking efficiency, output power, output current and computational complexity, etc. With inverse SEPIC DC converter, UOT boosts GMPP up to 3.8% to 3.9% while the tracking time is reduced or faster by 20.11% to 27.7% compared to PSO & TLBO techniques. It also shows 98.6% average tracking efficiency under considered PSCs. On the other hand, UOT achieves 4.22% to 5.5% higher GMPP and tracking time is reduced by 38.38% to 57.87% with the Luo DC converter compared to PSO & TLBO techniques. UOT shows 98.19% average tracking efficiency under considered PSCs with this converter. Moreover, UOT can also maintain 3.80% to 6.48% high output current in the SPV system under varying load conditions, with stabilization time reduced to 34.06% to 35.80% compared to PSO & TLBO techniques. The further practical applicability of the study is tested on a 300 W standalone SPV system. On this system, UOT again shows its better tracking performance by boosting 3.5% to 3.64% GMPP as well as the system output current. It also takes 30.86% less tracking time with 98.56% average tracking efficiency compared to PSO and TLBO. Experimental validation of all three metaheuristic approaches on low and high-rated systems confirms the supremacy of UOT over PSO & TLBO techniques in terms of tracking time, tracking efficiency, output power and current under different working scenarios.

Damage Identification of Suspension Footbridge Structures using New Hunting-based Algorithms

Metaheuristic algorithms have been applied to tackle challenging optimization problems in various domains, such as health, education, manufacturing, and biology. In particular, the field of Structural Health Monitoring (SHM) has received significant interest, particularly in the area of damage identification in structures. Popular optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Cuckoo Search (CS), Teaching Learning Based Optimization (TLBO), Artificial Hummingbird Algorithm (AHA), Moth Flame Optimizer (MFO), among others, have been employed to address this problem. However, notwithstanding the wide recognition of the current algorithms, their constraints are commonly acknowledged. Hence, this article advocates for the adoption of innovative hunting-inspired algorithms, namely the Ant Lion Optimizer (ALO), African Vulture Optimization Algorithm (AVOA), Grey Wolf Optimizer (GWO), Marine Predator Algorithm (MPA), and Whale Optimization Algorithm (WOA), which emulate the behaviors of wildlife species, to discern the areas and magnitudes of deterioration in a suspension footbridge. Moreover, in order to reduce computational time, only natural frequencies are applied as objective functions. The obtained results indicate that all the utilized algorithms can accurately detect the damages in the considered structure.

A blind color image watermarking algorithm based on Hadamard transform and TLBO algorithm

For the sake of better protect the copyright of color images, an efficient color image watermarking algorithm based on Hadamard transform and teaching-learning-based optimization (TLBO) algorithm is proposed in this paper. In the stage of embedding watermark, different watermark embedding strategies are employed in 4 × 4 blocks based on the impact level of pixel caused by the high and low bit of watermark and taking into account the energy concentration characteristics of the Hadamard matrix. To enhance the robustness of the watermarking algorithm, the voting system is designed for the high-four bits of watermark information. Simultaneously, the permutation ordered binary (POB) number system is used to compress the low-four bits of watermark information for improving the invisibility of the watermarked image and the embedding capacity. When the watermark needs to be extracted, the inverse process of POB number system transform is optimized, and the time of watermark extraction is shortened. Finally, the TLBO algorithm is used to optimize the watermarking algorithm parameters for saving the experimental time while considering both invisibility and robustness of the algorithm. In comparison to the other algorithms examined in this research, the experimental results demonstrate that the proposed watermarking algorithm exhibits several performance enhancements, these include aspects such as robustness, running time, watermark embedding capacity, security and invisibility.

Optimization of a CHP system using a forecasting dispatch and teaching-learning-based optimization algorithm

Using optimization algorithms and developing dispatch strategies are essential in sizing renewable energy systems to ensure optimal performance, cost-effectiveness, and sustainability. This study employs the Teaching-Learning-based Optimization (TLBO) algorithm to determine the optimal size of a Combined Heat and Power (CHP) system. The optimization results are validated using the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Furthermore, a novel dispatch strategy is developed to make an informed decision when using different energy sources. The strategy considers a 24-h foresight of upcoming electrical demand, solar irradiation, temperature, and wind speed. The developed dispatch strategy has led to a reduction in cost and excess electricity compared to the pre-prepared strategies. The energy sources employed include Photovoltaic panels (PV), Wind Turbines (WT), Diesel Generators (DG) with heat recovery capability, battery banks, and boilers to supply electrical and thermal demand. A Levelized cost of energy (LCOE) of 0.142 $/kWh is obtained for the PV/WT/DG/Battery/Boiler system. Although the three algorithms find almost similar optimal solutions, TLBO exhibits better convergence speed than PSO and GA. A comparison with HOMER software control strategies shows the developed dispatch strategy is 3.4% and 15.5% more efficient than Cycle Charging and Load Following strategies, respectively. Lastly, a comprehensive economic sensitivity analysis is performed to investigate the effect of inflation and discount rates on the size of components and final objective functions.

Hybrid Machine Learning and Modified Teaching Learning-Based English Optimization Algorithm for Smart City Communication

This paper introduces a hybrid algorithm that combines machine learning and modified teaching learning-based optimization (TLBO) for enhancing smart city communication and energy management. The primary objective is to optimize the modified systems, which face challenges due to their high population density. The proposed algorithm integrates the strengths of machine learning techniques, more specifically, the long short-term memory (LSTM) technique, with teaching learning-based optimization algorithms. To achieve optimization, the algorithm learns from historical data on energy consumption and communication patterns specific to the modeled system. By leveraging these insights, it can predict future energy consumption and communication patterns accurately. Additionally, the algorithm incorporates a modified teaching learning-based optimization approach inspired by the teaching and learning process in classrooms. It adjusts the system parameters based on feedback received from the system, thereby optimizing both energy consumption and communication systems. The effectiveness of the proposed algorithm is evaluated through a case study conducted on the test system, where historical data on energy consumption and communication patterns are analyzed. The results demonstrate that the algorithm efficiently optimizes the communication and energy management systems, leading to substantial energy savings and improved communication efficiency within the test system. In conclusion, this study presents a hybrid machine learning and modified teaching learning-based optimization algorithm that effectively addresses the communication and energy management challenges in the test system. Moreover, this algorithm holds the potential for application in various smart city domains beyond the test system. The findings of this research contribute to the advancement of smart city technologies and offer valuable insights into reducing energy consumption in densely populated urban areas.

Energy management of renewable based power grids using artificial intelligence: Digital twin of renewables

This paper proposes fog computing as a method of minimizing latency and maximizing performance by storing the information in the cloud to give a wide range of services to users using cloud computing. Also, consumer requests have been handled by several cloud Data Centers (DCs). Due to the fog's ability to attract more users and provide more services, load balancing has become increasingly crucial. It is therefore necessary to enhance the method that balances the fog's load. The study proposes a three-layer model comprising a cloud layer, a fog layer, and a user layer for optimal energy management in renewable power grids. The renewable energy sources are all modeled within the digital twin environment to make sure that very accurate monitoring is made. It is proposed for balancing the fog load using an artificial intelligence based optimization algorithm called the Whale optimization algorithm (WOA). Services are provided by fog servers in response to requests from users. In case of failure of the fog layer, all consumers' records are saved and services are provided to them through the cloud. Moreover, the service broker policies (SBP) have been applied to select the DCs efficiently. This paper compares the suggested algorithm to three previously developed algorithms including the particle swarm optimization (PSO), differential devolution (DE), and teaching–learning-based optimization (TLBO). It uses the three SBPs of closest data center, optimize response time (RT), reconfigure dynamically with load, and an improved SBP. It also minimizes RT and process time to improve the efficiency. The results show that there is an improvement in WOA's performance by approximately 5% compared to that of other algorithms.

Robust optimization of the design of monopropellant propulsion control systems using an advanced teaching-learning-based optimization method

This research proposes a novel approach for the robust optimization of the design of hydrogen peroxide propulsion control systems using the efficient and advanced Teaching-Learning-Based Optimization (TLBO) method. This study adopts a robust design optimization (RDO) formulation that considers both epistemic and aleatory uncertainties, including sparse points and interval data, and uses the Johnson distribution family for uncertainty representation. The maximum likelihood estimation method is applied to determine the distribution parameters, also considering interval data with a nested optimization technique. A novel advanced TLBO method with high accuracy and convergence rate is employed to optimization of this robust design approach. The method’s originality and advancement come from two categories of modifications to the original framework: the structure of the teaching and learning phases and the initialization and search approach. The efficacy and applicability of the proposed Ad-TLBO, respectively, were evaluated using benchmark problems from the CEC2020 competition and three real-world engineering problems, with a comparison to some recently published and the CEC competition’s top-ranked algorithms. The results and statistical analyses of the Quade test, Wilcoxon signed-rank test, and Friedman test show that the proposed Ad-TLBO method outperforms the other algorithms. The proposed optimization method is eventually applied to design a monopropellant propulsion system as the control actuator of a satellite orbital transfer system. It is found that the proposed advanced TLBO is effective in handling uncertainty in real design problems and improves both the convergence rate and accuracy of the optimization process.

Multi-Objective Optimal Power Flow Using a Modified Weighted Teaching-Learning Based Optimization Algorithm

The optimal power flow (OPF) problem is one of the most popular and important issues that researchers need to solve in power systems. In this study, a modified weighted teaching-learning-based optimization (WTLBO) algorithm was designed and implemented for the OPF problem, regarding the teaching-learning-based optimization (TLBO) algorithm, which is one of the meta-heuristic algorithms. To demonstrate the effectiveness of TLBO and developed WTLBO algorithms in OPF solutions, six different single/multi-objective functions consisting of targets, such as fuel cost, total active power losses, and voltage deviation were tested on standard IEEE 30 and 57 bus systems. Multi-objective functions were transformed into a single objective function using the weighted sum method. Analysis results were compared with different optimization algorithms used in the literature. When the developed WTLBO algorithm is compared according to the single/multi-objective functions in OPF solutions, it has been proven that it performs better than the original TLBO algorithm and other algorithms in the literature. As a result of the analysis, according to the TLBO algorithm, in the IEEE 30 bus power system, in the proposed WTLBO algorithm, a decrease of 0.05% in the total fuel cost, 2.20% in active power losses, and 13.37% in voltage deviation are observed.

Multi-Strategy Discrete Teaching–Learning-Based Optimization Algorithm to Solve No-Wait Flow-Shop-Scheduling Problem

To address the problems of the single evolutionary approach, decreasing diversity, inhomogeneity, and meaningfulness in the destruction process when the teaching–learning-based optimization (TLBO) algorithm solves the no-wait flow-shop-scheduling problem, the multi-strategy discrete teaching–learning-based optimization algorithm (MSDTLBO) is introduced. Considering the differences between individuals, the algorithm is redefined from the student’s point of view, giving the basic integer sequence encoding. To address the fact that the algorithm is prone to falling into local optimum and to leading to a reduction in search accuracy, the population was divided into three groups according to the learning ability of the individuals, and different teaching strategies were adopted to achieve the effect of teaching according to their needs. To improve the destruction-and-reconstruction process with symmetry, an iterative greedy algorithm of destruction–reconstruction was used as the main body, and a knowledge base was used to control the number of meaningless artifacts to be destroyed and to dynamically change the artifact-selection method in the destruction process. Finally, the algorithm was applied to the no-wait flow-shop-scheduling problem (NWFSP) to test its practical application value. After comparing twenty-one benchmark test functions with six algorithms, the experimental results showed that the algorithm has a certain effectiveness in solving NWFSP.

MOOTLBO: a new multi-objective observer–teacher–learner-based optimization

MOOTLBO: a new multi-objective observer–teacher–learner-based optimization