Version Of Particle Swarm Optimization Research Articles

A simheuristic approach towards supply chain scheduling: Integrating production, maintenance and distribution

This study attempts to integrate production, maintenance, and delivery operations among supply chain members. Despite numerous studies in the field of supply chain management, researchers have often overlooked crucial aspects, such as uncertainties in demand and production. For instance, the significant impact of maintenance activities on production flow has been underrepresented in supply chain management literature. This study investigates these gaps in the context of a fertilizer producer case study, which is characterized by seasonal demand and the functional silos syndrome due to old-fashioned management approaches. This study proposes a mathematical model and two multi-objective simheuristics for the Integrated Production, Maintenance, and Distribution Scheduling Problem (IPMDSP) considering demand variation for multiple products and product delivery time-windows using a heterogeneous fleet of vehicles. The IPMDSP is solved using the ϵ-constraint method and simheuristics linking the simulation model to customized and tuned versions of Particle Swarm Optimization (MOPSO) and the Non-dominated Sorting Genetic Algorithm (NSGA-II). The optimization objectives include minimizing maintenance duration, distribution costs, and customer dissatisfaction due to delivery tardiness. The results demonstrate the superiority of the simheuristic empowered by NSGA-II over the MOPSO in solving the IPMDSP. The comparison between the performance of deterministic and stochastic approaches in addressing the problem reveals that neglecting uncertainty caused by maintenance activities can lead to an increase in optimization objectives. Furthermore, the proposed simheuristics achieved significant improvements in minimizing objectives in solving the fertilizer producer case study.

Design of an Optimized Self-Acclimation Graded Boolean PSO with Back Propagation Model and Cuckoo Search Heuristics for Automatic Prediction of Chronic Kidney Disease

Objectives: A kind of Artificial Neural Network (ANN) known as a Back Propagation Neural Network (BPNN) has been extensively applied in a variety of sectors, including medical diagnosis, optical character recognition, stock market forecasting, and others. Many studies have employed BPNN to create decision-support tools for doctors to use while making clinical diagnoses. Chronic Kidney Disease (CKD) is one such kind of disease which has been receiving due importance from the past decades due to lack of symptoms in its early stages. The goal of this work is to demonstrate the performance of Artificial Intelligent (AI) algorithms in the early detection of CKD. Method: We received 800 patients’ real-time data from DY Patil Hospitals for this investigation. Self-Acclimation Graded Boolean PSO (SAG-BPSO), a modified version of Particle Swarm Optimization (PSO), has been proposed and used in this study to accomplish feature selection. Cuckoo Search Algorithm (CSA) has been used to optimise the weights and biases of the BPNN. Finally, this hybrid model is combined with BPNN for final predictions. Finally, a comparison is made between few state of art algorithms and the proposed approach. Results: The accuracy noted on applying BPNN on the dataset was approximately 91.45%. The combined model of BPNN+SAGBPSO provided an accuracy of about 92.25%. The accuracy achieved for the hybrid model of BPNN+SAGBPSO+CSA was approximately near to 98.07%. Conclusions: This research used SAGBPSO for feature selection and CSA for finalizing the weights and biases of BPNN. The research implemented BPNN, BPNN+SAGBPSO and BPNN+SAGBPSO+CSA on our real time dataset. The proposed hybrid model BPNN+SAGBPSO+CSA outperformed all the state of art deep learning algorithms in terms of performance metrics.

Role of transfer functions in PSO to select diagnostic attributes for chronic disease prediction: An experimental study

Particle Swarm Optimization (PSO) is a classic and popularly used meta-heuristic algorithm in many real-life optimization problems due to its less computational complexity and simplicity. The binary version of PSO, known as BPSO, is used to solve binary optimization problems, such as feature selection. Like other meta-heuristic optimization techniques designed on the continuous search space, PSO uses the transfer functions (TFs) to map the candidate solutions to the discrete search space in BPSO, and these TFs play a vital role to get the desired results. Over the years, many forms of TFs have been introduced in the literature, most of which fall under one of the five families - Linear, S-shaped, V-shaped, U-shaped, and Time-varying Mirrored S-shaped TFs. The goal of this study is to determine an appropriate setup constituting a TF and a classifier for feature selection from different types of clinical data. In this study, the impacts of the five TF families have been investigated, considering one from each family for the selection of attributes/features, while predicting disease using diagnosis or medical reports. The classification tasks are carried out using four standard classifiers: Support Vector Machine, Decision Tree, K-Nearest Neighbors, and Gaussian Naive Bayes. For experimental purposes, we have used four publicly available datasets namely, the UCI Heart Disease dataset, Wisconsin Breast Cancer dataset, UCI Chronic Kidney Disease dataset, and PIMA Indians Diabetes dataset. After an exhaustive set of experiments, we have obtained 96.72%, 99.82%, 100.00%, and 84.41% disease prediction scores in the best case for Heart disease, Breast Cancer, Chronic Kidney disease, and Diabetes, respectively. The obtained results are comparable to several state-of-the-art methods considered here for comparison. The present study helps in selecting a suitable BPSO setup (i.e., a TF and a classifier) to select important diagnostic attributes useful to design a computer-aided decision support system for the said diseases.

An efficient churn prediction model using gradient boosting machine and metaheuristic optimization

Customer churn remains a critical challenge in telecommunications, necessitating effective churn prediction (CP) methodologies. This paper introduces the Enhanced Gradient Boosting Model (EGBM), which uses a Support Vector Machine with a Radial Basis Function kernel (SVMRBF) as a base learner and exponential loss function to enhance the learning process of the GBM. The novel base learner significantly improves the initial classification performance of the traditional GBM and achieves enhanced performance in CP-EGBM after multiple boosting stages by utilizing state-of-the-art decision tree learners. Further, a modified version of Particle Swarm Optimization (PSO) using the consumption operator of the Artificial Ecosystem Optimization (AEO) method to prevent premature convergence of the PSO in the local optima is developed to tune the hyper-parameters of the CP-EGBM effectively. Seven open-source CP datasets are used to evaluate the performance of the developed CP-EGBM model using several quantitative evaluation metrics. The results showed that the CP-EGBM is significantly better than GBM and SVM models. Results are statistically validated using the Friedman ranking test. The proposed CP-EGBM is also compared with recently reported models in the literature. Comparative analysis with state-of-the-art models showcases CP-EGBM's promising improvements, making it a robust and effective solution for churn prediction in the telecommunications industry.

Risk-averse two-stage stochastic programming-based closed-loop supply chain network design under uncertain demand

In recent years, there has been a growing awareness and acceptance of the concept of green environmental protection among the public, primarily due to the worsening environmental issues and increased emphasis by governments on corporate social responsibility regulations. As a result, the management of closed-loop supply chains has gained significant attention from both businesses and researchers. Most stochastic programming models addressing closed-loop supply chain (CLSC) under uncertainty are typically designed to be risk-neutral. In this paper, a risk-averse two-stage stochastic programming (RATSSP) model for the closed-loop supply chain network design (CLSCND) problem considering facility capacity level decisions under uncertain demand. It is aiming to trade off the expected total profit and the profit risk, based on a given degree of responsive risk aversion. Due to the NP-hard nature of the CLSCND problem, this paper applies simplified swarm optimization (SSO), genetic algorithm (GA), and particle swarm optimization (PSO) algorithms to solve the problem. To improve the efficiency and quality of the obtained solutions, we propose a parallel computation meta-heuristic algorithm, parallel simplified swarm optimization (PSSO), along with parallel versions of PSO and GA. Computational results show the effectiveness of our proposed model, with PSSO demonstrating the best robustness and solution quality among the considered algorithms.

Boosted federated learning based on improved Particle Swarm Optimization for healthcare IoT devices

As healthcare data becomes increasingly available from various sources, including clinical institutions, patients, insurance companies, and pharmaceutical industries, machine learning (ML) services are becoming more significant in healthcare-facing domains. Therefore, it is imperative to ensure the integrity and reliability of ML models to maintain the quality of healthcare services. Particularly due to the growing need for privacy and security, healthcare data has resulted in each Internet of Things (IoT) device being treated as an independent source of data, isolated from other devices. Moreover, the limited computational and communication capabilities of wearable healthcare devices hinder the applicability of traditional ML. Federated Learning (FL) is a paradigm that maintains data privacy by storing only learned models on a server and advances with data from scattered clients, making it ideal for healthcare applications where patient data must be safeguarded. The potential of FL to transform healthcare is significant, as it can enable the development of new ML-powered applications that can enhance the quality of care, lower costs, and improve patient outcomes. However, the accuracy of current Federated Learning aggregation methods suffers greatly in unstable network situations due to the high volume of weights transmitted and received. To address this issue, we propose an alternative approach to Federated Average (FedAvg) that updates the global model by gathering score values from learned models primarily utilized in Federated Learning, using an improved version of Particle Swarm Optimization (PSO) called FedImpPSO. This approach boosts the robustness of the algorithm in erratic network conditions. To further enhance the speed and efficiency of data exchange within a network, we modify the format of the data clients send to servers using the FedImpPSO method. The proposed approach is evaluated using the CIFAR-10 and CIFAR-100 datasets and a Convolutional Neural Network (CNN). We found that it yielded an average accuracy improvement of 8.14% over FedAvg and 2.5% over Federated PSO (FedPSO). This study evaluates the use of FedImpPSO in healthcare by training a deep-learning model over two case studies to evaluate the effectiveness of our approach in healthcare. The first case study involves the classification of COVID-19 using public datasets (Ultrasound and X-ray) and achieved an F1-measure of 77.90% and 92.16%, respectively. The second case study was conducted over the cardiovascular dataset, where our proposed FedImpPSO achieves 91.18% and 92% accuracy in predicting the existence of heart diseases. As a result, our approach demonstrates the effectiveness of using FedImpPSO to improve the accuracy and robustness of Federated Learning in unstable network conditions and has potential applications in healthcare and other domains where data privacy is critical.

Parallel Global Best-Worst Particle Swarm Optimization Algorithm for solving optimization problems

The range of applications of swarm optimization algorithms is very vast. However, high dimensions and more number of decision variables make these optimization problems more complex. Particle Swarm Optimization (PSO) is the most popular optimizer for performing such types of optimization. PSO is motivated from the movement and intelligence of swarms. However, the primary constraint with the PSO and other swarm algorithms is enormous computational time (CT) due to more number of decision variables in complex problem. The number of steps inside Swarm Intelligence Algorithms (SIAs) also increase the complexity of computation in the process of optimization. Many iterations of the procedure of SIA need more CT since these algorithms are iterative in nature. In this study, a new Global Best-Worst Particle Swarm Optimization (GBWPSO) algorithm has been proposed so as to provide a fully version of parallel algorithm. GBWPSO algorithm is the combination of PSO and Jaya algorithm that provides a refined version of parallel algorithm having more parallelism. The proposed algorithm is executed on three different computational hardware with various combinations of population size and maximum number of iteration on five different standard benchmark functions. The evaluation is done on the basis of performance metrics such as speedup (S), real speedup (RS), maximum speedup (MS), efficiency (E), and scalability. The proposed parallel algorithm (P-GBWPSO) outperforms both parallel version of PSO and Jaya algorithm in terms of less CT and better optimal solution. Based on the results, we found that system 03 (S3) is best on proposed GBWPSO algorithm with an efficiency of 1.2518 compare with system 01 (S1) and system 02 (S2).

Solving redundant inverse kinematics of CMOR based on chaos-driven particle swarm optimization algorithm

The China Fusion Engineering Test Reactor (CFETR) multipurpose overload robot (CMOR) is used to maintain the vacuum chamber inner components damaged by heat loads, electromagnetic fields, and nuclear radiation. The CMOR is a redundant robot whose configuration does not meet the condition of the Pieper criterion. Redundant kinematic problems can only be solved by numerical methods, not analytical methods. Conventional numerical iterative methods include considerable computational load, accumulated errors, and the Jacobian matrix's singularity. To solve redundant inverse kinematics efficiently, we compared the behaviors of 25 versions of particle swarm optimization (PSO) algorithms with 12 one-dimensional chaotic maps under unimodal and multimodal test functions. Moreover, we selected three chaos-driven PSO algorithms with optimal convergence performance to address CMOR inverse kinematics. The experimental results indicated that chaos-driven PSO algorithms have higher computational efficiency and can effectively improve the speed and accuracy of algorithm convergence. This proposed algorithm delivers a novel and efficient method for inverse kinematics of redundant robots based on chaotic maps.

Near-unity uniformity and efficiency broadband meta-beam-splitter/combiner.

Subwavelength planar structured interfaces, also known as metasurfaces, are ultra-thin optical elements modulating the amplitude, phase, and polarization of incident light using nanostructures called meta-atoms. The optical properties of such metasurfaces can be controlled across wavelengths by selecting geometries and materials of the meta-atoms. Given recent technological developments in optical device miniaturization, components for beam splitting and beam combining are sought for use within these devices as two quintessential components of every optical setup. However, realizing such devices using metasurfaces typically leads to poor uniformity of diffraction orders and narrow-band operation. Using a modified version of particle swarm optimization, we propose and numerically demonstrate a broadband, reciprocal metasurface beam combiner/splitter with uniformity > 97% and diffraction efficiency > 90% in the continuous band from λ=1525 nm to λ=1575 nm. The proposed approach significantly extends the current state of the art of metasurfaces design in terms of uniformity, bandwidth, and efficiency, and opens the door for devices requiring high power or near-unit uniformity.

Reconstruction of delay differential equations via learning parameterized dictionary

Reconstructing the governing equations of unknown systems plays an important role in physics. Sparse modeling methods, such as sparse identification of nonlinear dynamical systems (SINDy), provide a very good framework to tackle this problem. However, SINDy struggles with discovering delay differential equations (DDEs) due to the curse of dimensionality in the candidate dictionary. That is, each possible discrete delay term in the dictionary should be regarded as an independent basis element, which makes the combination of exhaustive discrete delay terms blow up. To deal with the curse of dimensionality and further reconstruct DDEs, a variant method of SINDy is presented in this paper in which a parameterized dictionary is proposed. In the parameterized dictionary, undetermined parameter variables are included in the candidate function to cover a wide range of different functions. Then, the reconstruction problem is reformulated as mixed-integer nonlinear programming (MINLP) and solved by a bi-level algorithm in which the binary version and the continuous version of particle swarm optimization (PSO) are respectively adopted in two levels. Simulation experiments are performed in 5 test systems including 3 well-known chaotic DDEs such as Mackey–Glass system. The results validate the effectiveness of the presented method.

A Robust Renewable Energy Source-oriented Strategy for Smart Charging of Plug-in Electric Vehicles Considering Diverse Uncertainty Resources

Nowadays, the notion of plug-in electric vehicle (PEV) as a valuable tool of energy management has been extensively employed in smart distribution grids. The main advantage of clean energy as well as elastic behaviour of operation in both electrical load/generation modes can sufficiently justify the utilization of such emerging technology. Moreover, the specific capability of renewable energy sources (RESs) in terms of contribution in PEV smart charging/discharging scheme would cause to remarkable techno-economic benefits in smart grids. However, the load demand, RES generation and also the electrical energy price encounter with uncertainty in practice required to be properly handled. Hence, a non-deterministic optimization model based on information gap decision theory (IGDT) is proposed in this paper to specify a robust PEV smart charging pattern. To solve the multi-objective proposed IGDT-based PEV smart charging (IGDT-PSC) model, the multi-objective version of particle swarm optimization (MOPSO) is utilized to define a set of Pareto optimal solutions. Furthermore, the final solution among the Pareto solutions is selected by means of a linear fuzzy satisfaction rule. The simulation results for a test smart microgrid comprising a PEV, a set of RES units and a load demand verify the effectiveness of the proposed IGDT-PSC model.

BN-GEPSO: Learning Bayesian Network Structure Using Generalized Particle Swarm Optimization

At present Bayesian Networks (BN) are being used widely for demonstrating uncertain knowledge in many disciplines, including biology, computer science, risk analysis, service quality analysis, and business. But they suffer from the problem that when the nodes and edges increase, the structure learning difficulty increases and algorithms become inefficient. To solve this problem, heuristic optimization algorithms are used, which tend to find a near-optimal answer rather than an exact one, with particle swarm optimization (PSO) being one of them. PSO is a swarm intelligence-based algorithm having basic inspiration from flocks of birds (how they search for food). PSO is employed widely because it is easier to code, converges quickly, and can be parallelized easily. We use a recently proposed version of PSO called generalized particle swarm optimization (GEPSO) to learn bayesian network structure. We construct an initial directed acyclic graph (DAG) by using the max-min parent’s children (MMPC) algorithm and cross relative average entropy. This DAG is used to create a population for the GEPSO optimization procedure. Moreover, we propose a velocity update procedure to increase the efficiency of the algorithmic search process. Results of the experiments show that as the complexity of the dataset increases, our algorithm Bayesian network generalized particle swarm optimization (BN-GEPSO) outperforms the PSO algorithm in terms of the Bayesian information criterion (BIC) score.

1.5D Time Domain Electromagnetic Inversion Using Global Optimization Methods and Parallel Processing

An innovative comparative study among three different global optimization methods (GOMs) to invert time domain electromagnetic data (TDEM), was applied in the 1.5D subsurface resistivity imaging. These stochastic methods allow the incorporation of different kinds of constraints in the objective function, due to the easiness of implementation in their algorithms and their computational efficiency. Nevertheless, global optimization methods, like any other based on swarm intelligence cost much computational time, including problems with a high number of unknown parameters and when the forward modeling involves time-consuming calculations such as Gaver-Stehfest inverse transforms, Hankel transforms, and others. To overcome this difficulty, we developed a parallel pure MPI version of each GOM, allowing the distribution of the computation among several cores of a cluster. The performances of the classic version of particle swarm optimization (PSO), grey wolf optimizer (GWO), and whale optimization algorithm (WOA) using MPI parallelism for solving 1.5D TDEM inverse problems are compared here in a set of synthetic and real data. The principal outcomes show: (1) These GOMs reproduce quite well the distribution of subsurface resistivity either synthetic models or real data, (2) WOA and PSO exhibit better computational performances, converging first than GWO, (3) WOA provided better performance in the final value achieved of the cost function than PSO and GWO, and (4) pure MPI parallelism provided a 17x and 50x speedup in the computation time for both synthetic and real data, respectively. In a better way to classify this comparison, we analyzed the solutions using total variation (TV) and Global smoothness (GS) constraints, to identify smooth and sharp structures. Additionally, we have diminished the computational time execution with the parallel solution (MPI version of each stochastic traditional method) against the sequential processing.

Limitations of Nature-Inspired Algorithms for Pricing on Digital Platforms

Digital platforms have begun to rely more on algorithms to perform basic tasks such as pricing. These platforms must set prices that coordinate two or more sides that need each other in some way (e.g., developers and users or buyers and sellers). Therefore, it is essential to form correct expectations about how both sides behave. The purpose of this paper was to study the effect of different levels of information on two biology-inspired metaheuristics (differential evolution and particle swarm optimization algorithms) that were programmed to set prices on multisided platforms. We assumed that one platform always formed correct expectations (human platform) while the competitor always used a generic version of particle swarm optimization or differential evolution algorithms. We tested different levels of information that modified how expectations were formed. We found that both algorithms might end up in suboptimal solutions, showing that algorithms needed to account for expectation formation explicitly or risk setting nonoptimal prices. In addition, we found regularity in the way algorithms set prices when they formed incorrect expectations that can help practitioners detect cases in need of intervention.

Generating exact optimal designs via particle swarm optimization: Assessing efficacy and efficiency via case study

In this study we address existing deficiencies in the literature on applications of Particle Swarm Optimization (PSO) to generate optimal designs. We present the results of a large computer study in which we bench-mark both efficiency and efficacy of PSO to generate high quality candidate designs for small-exact response surface scenarios commonly encountered by industrial practitioners. A preferred version of PSO is demonstrated and recommended for generating optimal experimental designs. Further, in contrast to popular local optimizers such as the coordinate exchange, PSO is demonstrated to, even in a single run, generate highly efficient designs with large probability at small computing cost. Therefore, it appears beneficial for more practitioners to adopt and use PSO as tool for generating candidate experimental designs.

Day‐Ahead Multi‐Objective Microgrid Dispatch Optimization Based on Demand Side Management Via Particle Swarm Optimization

The rapid growth of electricity demands for recent years leads to many global concerns, including greenhouse, energy risk, and large size brownouts, which require modern energy management system to provide environmental‐friendly power supply service with less cost and higher reliability. However, most of previous studies separately focus on improving prediction accuracy or reducing cost and emission of power supply solution by dispatch optimization. To exploit the benefits of microgrid system furthermore, this paper firstly proposes a comprehensive day‐ahead multi‐objective microgrid optimization framework that combines forecasting technology, demand side management (DSM) with economic and environmental dispatch (EED) together. Then, two versions of particle swarm optimization are implemented for obtaining load control plan from DSM model and power supply plan from EED model, respectively. Moreover, two different microgrids' applied scenarios are simulated with detailed sensitivities analysis on key parameters. Experiment results demonstrate effectiveness of the proposed framework, which can obtain load demands profile with better reliability, as well as power supply solution with less cost and lower emission. Meanwhile, beneficial decision supports are provided to manager for their references. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Implementation of Modified Multi-Objective Particle Swarm Optimization to multi-machine power system stability

This paper addresses a coordinated wind turbine equipped with a doubly fed induction generator, photovoltaic plant, and a fractional lead-lag stabilizer to enhance the low-frequency oscillation (LFO) in response to their effects on the power system under load conditions. To achieve better stability in the power system, the DFIG wind turbine and PV plant are equipped with a power oscillation damper (POD) in a practical form by single-input signal output. Although power system stabilizers (PSSs) are the first choice offered for multimachine power system stability, they cannot guarantee power system stability with different types of power plants and their interconnections. To get the high-speed response and the least steady-state error, the fractional-order lead-lag stabilizer is suggested. To save the initial price of equipping all generators with the stabilizer, only the best locations are selected using a probabilistic method formulated in the frequency domain. The optimal tuning of FL-L and POD variables is formulated by the optimization problem, and the developed version of particle swarm optimization is used to solve it. Finally, the suggested coordinated controller is implemented in the MATLAB tool. Moreover, the suggested controller is compared to other available controllers by several indices. The simulation results show that the proposed controller can provide acceptable performance in low frequency oscillation in large scale power systems. According to the simulation results, the damping ratio was modified from 0.1343 to 0.5773. It leads to a reduction in the settlement time from 3.65 s to 2.14 s. Also, the proposed modified optimization algorithm makes a faster and better global and local search compared to other optimization algorithms.

Coverage Optimization of Wireless Sensor Networks Using Combinations of PSO and Chaos Optimization

The coverage rate is the most crucial index in wireless sensor networks (WSNs) design; it involves making the sensors with a reasonable distribution, which closely relates to the quality of service (QoS) and survival period of the entire network. This article proposes to use particle swarm optimization (PSO) and chaos optimization in conjunction for the coverage optimization. All sensor locations are encoded together as a particle position. PSO was used first to make sensors move close to their optimal positions; furthermore, a variable domain chaos optimization algorithm (VDCOA) was employed to reach a higher coverage rate, along with improved evenness and average moving distance. Six versions of VDCOA, taking circle, logistic, Gaussian, Chebyshev, sinusoidal and cubic maps, respectively, were investigated. The simulation experiment tested three cases: square, rectangular and circular regions using nine algorithms: six versions of PSO plus VDCOA, PSO and other two PSO variants. All six versions showed better performance than PSO and CPSO, with coverage all exceeding 90% for the first two cases. Moreover, one version, PSO plus circle map (PSO-Circle), increased the coverage rate by 3.17%, 2.41% and 12.94% compared with PSO in three cases, respectively, and outperformed the other eight algorithms.

Intelligent fault diagnosis for power distribution system-comparative studies

Short circuit is one of the most popular types of permanent fault in power distribution system. Thus, fast and accuracy diagnosis of short circuit failure is very important so that the power system works more effectively. In this paper, a newly enhanced support vector machine (SVM) classifier has been investigated to identify ten short-circuit fault types, including single line-to-ground faults (XG, YG, ZG), line-to-line faults (XY, XZ, YZ), double line-to-ground faults (XYG, XZG, YZG) and three-line faults (XYZ). The performance of this enhanced SVM model has been improved by using three different versions of particle swarm optimization (PSO), namely: classical PSO (C-PSO), time varying acceleration coefficients PSO (T-PSO) and constriction factor PSO (K-PSO). Further, utilizing pseudo-random binary sequence (PRBS)-based time domain reflectometry (TDR) method allows to obtain a reliable dataset for SVM classifier. The experimental results performed on a two-branch distribution line show the most optimal variant of PSO for short fault diagnosis.

A sequential hybridization of ETLBO and IPSO for solving reserve‐constrained combined heat, power and economic dispatch problem

The explosive demand for electricity and ecological concerns has necessitated the operation of power networks in a more cost-effective approach. In recent years, the integration of combined heat and power units has presented a potential answer to these problems; nevertheless, a new difficult challenge has emerged: finding an optimal solution for simultaneous dispatch of power and heat. Therefore, to tackle this problem, this work presents an intelligent sequential algorithm based on a hybridization of an enthusiasm-aided teaching and learning-based optimization algorithm (ETLBO) with an improved version of particle swarm optimization (IPSO). The proposed method can simultaneously minimize total generating costs while considering a variety of physical and operational limitations. In addition, this research designed an adaptive violation constraint management approach combined with the formulated hybridized optimization algorithm to ensure system constraints' safe preservation during the optimization process. Finally, the performance of the proposed method is compared to the recently developed metaheuristic algorithms as well as Knitro and IPOPT (industrially used optimization packages), in which the ETLBO-IPSO outperforms all the other methods.