Alternative Optimization Research Articles

Enhancing personalized learning with explainable AI: A chaotic particle swarm optimization based decision support system

In the realm of Educational Technology, personalized learning is pivotal, yet predicting students' learning abilities based on learning styles and ICT remains challenging. We propose a decision support system using Machine Learning (ML), swarm intelligence, and explainable artificial (XAI) techniques to assess students' performance. Our model employs Chaotic Particle Swarm Optimization (C-PSO) with Henon execution, outperforming Genetic Algorithm (GA), Ant Colony Optimization (ACO), Firefly Algorithm (FA), Bee Colony Optimization (BCO), Artificial Fish Swarm Algorithm (AFSA), Mayfly Optimization Algorithm (MFOA), Mother Optimization Algorithm (MOA), Fuzzy Self-Tuning PSO (FST-PSO). Evaluating efficiency, effectiveness, and solution quality reveals C-PSO's superiority. The study identifies the significant impact of ICT on self-progress and employs Spearman Rank correlation for statistical validation. Findings suggest C-PSO as an effective tool for optimizing educational data analysis and decision-making. Further exploration in real-world educational settings and comparative analyses with alternative optimization techniques are recommended for future research.

A framework for constrained large-scale multi-objective white-box problems based on two-scale optimization through decision transfer

Most existing constrained multi-objective evolutionary algorithms (CMOEAs) are not so efficient when handling constrained large-scale multi-objective problems (CLSMOPs). To overcome white-box CLSMOPs with definitive objective functions, a two-scale optimization framework based on decision transfer, which integrates dimensionality reduction of large-scale decision variables and constraint handling technology, is proposed. The Lagrange multiplier is first used to construct the two-scale optimization model, which bridges original large-scale decision space of variables and small-scale (2-scale) decision space of objective-constraint parameter. The decision transfer algorithm is then designed to switch between large-scale original decision space and small-scale parametric decision space, while achieving the maximum dimensionality reduction. Finally, the two-scale evolution strategy is proposed for the alternative optimizations in the two decision spaces, which emphasize objectives and constraints, respectively. In summary, the optimization in the large-scale space pushes the population to unconstrained Pareto front (PF), the optimization in the small-scale space helps the population cross the infeasible areas to approach constrained PF, and the offspring generation by Lagrange multiplier is beneficial to both objectives and constraints. Eight representative and state-of-the-art CMOEAs have been embedded into the CLDTEA framework to demonstrate its effectiveness through comparative experiments on CLSMOPs with equalityandinequalityconstraints and 1000 decision variables. Experimental results show that CLDTEA can significantly improve the performance of these basic CMOEAs.

Data-driven surrogate model for aerodynamic design using separable shape tensor method

In the context of increasing dimensionality of design variables and the complexity of constraints, the efficacy of Surrogate-Based Optimization (SBO) is limited. The traditional linear and nonlinear dimensionality reduction algorithms are mainly to decompose the mathematical matrix composed of design variables or objective functions in various forms, the smoothness of the design space cannot be guaranteed in the process, and additional constraint functions need to be added in the optimization, which increases the calculation cost. This study presents a new parameterization method to improve both problems of SBO. The new parameterization is addressed by decoupling affine transformations (dilation, rotation, shearing, and translation) within the Grassmannian submanifold, which enables a separate representation of the physical information of the airfoil in a high-dimensional space. Building upon this, Principal Geodesic Analysis (PGA) is employed to achieve geometric control, compress the design space, reduce the number of design variables, reduce the dimensions of design variables and enhance predictive performance during the surrogate optimization process. For comparison, a dimensionality reduction space is defined using 95% of the energy, and RAE 2822 for transonic conditions are used as demonstrations. This method significantly enhances the optimization efficiency of the surrogate model while effectively enabling geometric constraints. In three-dimensional problems, it enables simultaneous design of planar shapes for various components of the aircraft and high-order perturbation deformations. Optimization was applied to the ONERA M6 wing, achieving a lift-drag ratio of 18.09, representing a 27.25% improvement compared to the baseline configuration. In comparison to conventional surrogate model optimization methods, which only achieved a 17.97% improvement, this approach demonstrates its superiority.

Nanophotonic phased array XY Hamiltonian solver

Solving large-scale computationally hard optimization problems using existing computers has hit a bottleneck. A promising alternative approach uses physics-based phenomena to naturally solve optimization problems, wherein the physical phenomena evolve to their minimum energy. In this regard, photonics devices have shown promise as alternative optimization architectures, benefiting from high-speed, high-bandwidth, and parallelism in the optical domain. Among photonic devices, programmable spatial light modulators (SLMs) have shown promise in solving large scale Ising model problems, to which many computationally hard problems can be mapped. Despite much progress, existing SLMs for solving the Ising model and similar problems suffer from slow update rates and physical bulkiness. Here, we show that using a compact silicon photonic integrated circuit optical phased array (PIC-OPA), we can simulate an XY Hamiltonian, a generalized form of the Ising Hamiltonian, where spins can vary continuously. In this nanophotonic XY Hamiltonian solver, the spins are implemented using analog phase shifters in the optical phased array. The far field intensity pattern of the PIC-OPA represents an all-to-all coupled XY Hamiltonian energy and can be optimized with the tunable phase-shifters, allowing us to solve an all-to-all coupled XY model. Our results show the utility of PIC-OPAs as compact, low power, and high-speed solvers for nondeterministic polynomial-hard problems. The scalability of the silicon PIC-OPA and its compatibility with monolithic integration with CMOS electronics further promise the realization of a powerful hybrid photonic/electronic non-Von Neumann compute engine.

Implementation of FOPID controller with modified harmony search optimization for precise modelling and auto-tuning of nonlinear systems

This research aims to optimize the fractional order proportional integral derivative (FOPID) controller for nonlinear process tests. Instead of relying on the traditional trial-and error method for dynamic parameter selection, this proposes a modified harmony search optimization method to compute the optimal dynamic parameters. Based on the physical parameters, using mathematical modelling techniques system transfer function is determined. Additionally, the delay of the control scheme is determined using the Open Loop Transfer Function (OLTF) response. The dynamic parameters of the FOPID controller are evaluated using the Harmonic Search Algorithm – Fractional Order Proportional Integral Derivative (HAS_FOPID) optimization technique, which aims to minimize the system's Integral Square Error (ISE). Time responses, including rise time, peak time, and peak overshoot, are obtained and optimized through the HAS_FOPID algorithm. To verify systems stability, the output response is analysed using various techniques such as bode plots, pole placement, and Nyquist plots. Furthermore, the HAS_FOPID optimization technique is compared with other natural optimization techniques to assess its effectiveness. The research also evaluates the system's robustness by supply/ load disturbances. The objective is to demonstrate that, the optimized HAS_FOPID technique, has significantly enhanced control performance, stability, and robustness of nonlinear systems compared to natural and alternative optimization approaches.

A comparative study of advanced evolutionary algorithms for optimizing microgrid performance under dynamic pricing conditions

The integration of microgrids into the existing power system framework enhances the reliability and efficiency of the utility grid. This manuscript presents an innovative mathematical paradigm designed for the optimization of both the structural and operational aspects of a grid-connected microgrid, leveraging the principles of Demand-Side Management (DSM). The focus of this work lies in a comprehensive exploration of the implications brought about by the Renewable Generation-Based Dynamic Pricing Demand Response (RGDP-DR) mechanism, particularly in terms of its influence on the optimal microgrid configuration, considering perspectives from end-users and the utility entity. This inquiry is rooted in a holistic assessment that encompasses technical and economic performance benchmarks. The RGDP-induced DR framework adeptly addresses the needs of the consumer base, showcasing notable efficiency and economic feasibility. To address the intricate nonlinear optimization challenge at hand, we employ an evolutionary algorithm named the "Dandelion Algorithm" (DA). A rigorous comparative study is conducted to evaluate the efficacy of four optimization techniques, affirming the supremacy of the proposed DA. Within this discourse, the complexity of microgrid sizing is cast as a dual-objective optimization task. The twin objectives involve minimizing the aggregate annual outlay and reducing emissions. The results of this endeavor unequivocally endorse the superiority of the DA over its counterparts. The DA demonstrates exceptional proficiency in orchestrating the most cost-effective microgrid and consumer invoice, surpassing the performance of alternative optimization methodologies.

Using simulations to compare the current Davis Cup ranking system to Elo.

The Davis Cup is the premier men's team event in tennis, run by the International Tennis Federation and in which over 130 nations compete. It uses a merit-based ranking system that allows nations to gain a pre-determined number of points when they win. The rankings are integral to the competition structure and used in the draws of every round. Therefore, it is essential that the ranking method performs well with respect to required performance criteria of the International Tennis Federation. The Elo rating method is a commonly used method of rating and ranking participants in a competitive exercise and is used by FIFA for the ranking of male and female national teams. The performance of the current Davis Cup ranking method and Elo rating were compared using a simulation of the Davis Cup competition structure. Four criteria were used for the comparison: Finishing Order Correlation, Skill Level Correlation, Responsiveness, and Protection. Such a comparison has not previously been published. The two methods were comparable across three of the criteria, with the Davis cup easily outperforming Elo in responsiveness. Indeed, the Elo method had such poor responsiveness that improved performance may not be fully recognised within a player's career. An alternative method of optimising the Elo K-factor parameter was developed and this improved the performance of Elo to match the current Davis Cup method. In conclusion, the current Davis Cup ranking method is performing at a standard that cannot be matched by typically optimised Elo but can be matched when an alternative optimisation method is used. Therefore, no evidence was found to suggest that the current Davis Cup ranking method could be improved upon by using Elo. However, alternative K-factor optimisation methods should be considered when applying Elo to a competition.

Design analysis of hybrid solar-wind renewable energy systems using water strider optimization

Hybrid energy systems (HES) have the potential to reduce global warming and provide an affordable alternative to provide electricity access to remote regions. This paper proposes an optimal design of HES using water strider optimisation (WSO) with multi-objective parameters. A selection of appropriate components, sizing, radius index, pitch control, and battery bank size for a HES system is optimised with the WSO algorithm. The objective is to provide cost-efficient, dependable, and alternative energy optimisation in various load demand conditions. The designed parameters, including a battery bank of 2.5 kWh to 800 kWh, a solar photovoltaic (PV) array size of 50 kW to 300 kW in terms of load demand, a 2-axis tracking PV system with an area of 12 m2, and a wind turbine (WT) with a swept area of 22 m2 and a hub height of 12.3 m, make up an optimised HES. The single-family energy demand in India is the topic of this study, with a lifespan of 20 to 30 years.

Optimizing [formula omitted] capture in pressure swing adsorption units: A deep neural network approach with optimality evaluation and operating maps for decision-making

This study presents a methodology for surrogate optimization of cyclic adsorption processes, focusing on enhancing pressure swing adsorption units for carbon dioxide (CO2) capture. We developed and implemented a multiple-input, single-output (MISO) framework comprising two deep neural network (DNN) models, predicting key process performance indicators. These models were then integrated into an optimization framework, leveraging particle swarm optimization (PSO) and statistical analysis to generate a comprehensive Pareto front representation. This approach delineated feasible operational regions (FORs) and highlighted the spectrum of optimal decision-making scenarios. A key aspect of our methodology was the evaluation of optimization effectiveness. This was accomplished by testing decision variables derived from the Pareto front against a phenomenological model, affirming the surrogate model’s reliability. Subsequently, the study delved into analyzing the feasible operational domains of these decision variables. A detailed correlation map was constructed to elucidate the interplay between these variables, thereby uncovering the most impactful factors influencing process behavior. The study offers a practical, insightful operational map that aids operators in pinpointing the optimal process location and prioritizing specific operational goals.

An efficient, fast, and robust algorithm for single diode model parameters estimation of photovoltaic solar cells

AbstractParameter estimation of photovoltaic (PV) solar cells and module models pays attention to researchers owing to their importance in practical considerations. The single diode model (SDM) circuit with five unknown parameters is widely used to model PV solar cells and modules. In this paper, a novel approach called alternate optimization (AO) algorithm based on a discrete search is proposed to estimate the SDM parameters. The proposed algorithm provides efficient and robust performance, considering a limited set of discrete values and increasing the convergence speed. Two practical case studies with actual measurements are considered to assess the proposed AO algorithm: the RTC France solar cell and monocrystalline PV modules with different irradiations and temperatures. The numerical findings underscore the superior performance of the proposed AO algorithm across various metrics. Notably, it achieves an exceptional Root Mean Square Error (RMSE) of 7.7426 × 10−04 for the RTC France PV cell and approximately 1 × 10−03 RMSE for monocrystalline PV modules. Additionally, the algorithm exhibits unparalleled speed, showcasing the fastest convergence with an elapsed time of 1.66 × 10−05—markedly 4.45 times quicker than the fastest method documented in the literature for SDM parameter estimation. Furthermore, the proposed AO algorithm stands out for its efficiency, requiring a maximum of five iterations for parameter estimation, a substantial improvement compared to the more than 10 iterations typically needed by algorithms in the existing literature. Its robustness is also commendable, as evidenced by the stability of final RMSE values across a variety of experiments, distinguishing it from less robust algorithms found in the literature.

Adaptive Point-Line Fusion: A Targetless LiDAR-Camera Calibration Method with Scheme Selection for Autonomous Driving.

Accurate calibration between LiDAR and camera sensors is crucial for autonomous driving systems to perceive and understand the environment effectively. Typically, LiDAR-camera extrinsic calibration requires feature alignment and overlapping fields of view. Aligning features from different modalities can be challenging due to noise influence. Therefore, this paper proposes a targetless extrinsic calibration method for monocular cameras and LiDAR sensors that have a non-overlapping field of view. The proposed solution uses pose transformation to establish data association across different modalities. This conversion turns the calibration problem into an optimization problem within a visual SLAM system without requiring overlapping views. To improve performance, line features serve as constraints in visual SLAM. Accurate positions of line segments are obtained by utilizing an extended photometric error optimization method. Moreover, a strategy is proposed for selecting appropriate calibration methods from among several alternative optimization schemes. This adaptive calibration method selection strategy ensures robust calibration performance in urban autonomous driving scenarios with varying lighting and environmental textures while avoiding failures and excessive bias that may result from relying on a single approach.

Alter codon bias of the P. pastoris genome to overcome a bottleneck in codon optimization strategy development and improve protein expression

Apart from its role in translation, codon bias is also an important mechanism to regulate mRNA levels. The traditional frequency-based codon optimization strategy is rather efficient in organisms such as N. crassa, but much less in yeast P. pastoris which is a popular host for heterologous protein expression. This is because that unlike N. crassa, the preferred codons of P. pastoris are actually AU-rich and hence codon optimization for extremely low GC content comes with issues of pre-mature transcriptional termination or low RNA stability in spite of translational advantages. To overcome this bottleneck, we focused on three reporter genes in P. pastoris first and confirmed the great advantage of GC-prone codon optimization on mRNA levels. Then we altered the codon bias profile of P. pastoris by introducing additional rare tRNA gene copies. Prior to that we constructed IPTG-regulated tRNA species to enable chassis cells to switch between different codon bias status. As demonstrated again with reporter genes, protein yield of luc and 0788 was successfully increased by 4–5 folds in chassis cells. In summary, here we provide an alternative codon optimization strategy for genes with unsatisfactory performance under traditional codon frequency-based optimization.

Discriminative and robust least squares regression for semi-supervised image classification

Due to the ability to leverage information from both unlabeled and labeled data, semi-supervised classification has found extensive applications in various practical scenarios. However, there are two major drawbacks: (1) Traditional graph construction leads to high algorithmic complexity, which limits efficiency. (2) The decision boundary might be blurred by boundary points in common cases. To cope with these issues, inspired by classical Least Squares Regression (LSR), we present a novel semi-supervised classification algorithm termed as Discriminative and Robust LSR (DRLSR) for semi-supervised image classification. First of all, a manifold regularization term is designed and introduced to an LSR-based semi-supervised method to preserve local manifold structures and smooth structures of the subspace, which strengthens the discrimination ability. Meanwhile, preservation of local manifold structures also contributes to restrain decision boundaries from being blurred by boundary points, which strengthens robustness of the algorithm. After that, an efficient alternative optimization method is applied to our algorithm. Evidence of the effectiveness of DRLSR are compelled by extensive experimental results.

Advancing microgrid efficiency: a study on battery storage systems and wind energy penetration based on a Contracted fitness-dependent optimization algorithm

ABSTRACT This research study presents a novel approach to enhance the efficiency and performance of Battery Energy Storage Systems (BESSs) within microgrids, focusing particularly on the integration of wind energy. The inherent inconsistency and unpredictability of Renewable Energy Resources (RERs) necessitate the development of effective integration solutions to accommodate them. Our article provides a customized iteration of the metaheuristic algorithm referred to as the Contracted fitness-dependent optimizer. The proposed methodology involves the adaptive adjustment of migration rates based on habitat suitability indices, while also considering variations in perturbations. The incorporation of Lévy flight and an elimination phase significantly enhances the algorithm’s efficacy in problem-solving. In order to establish the superiority of our strategy over alternative optimization approaches, we conduct simulations across diverse conditions and subsequently compare the outcomes. It was calculated that the daily scheduling cost was 235.2$ and the daily operational cost was USD 268$. In Scenario 3, the total operating expenses and arranging costs for 1 day, respectively, were determined to be 165$ and 115$. Further, the highest depth of discharge level was around 77%. Moreover, a −9.18 kW and 23.02 kW maximum charging and discharging power adjustment was made. The findings of our study emphasize the economic and operational benefits associated with appropriately sized BESSs within microgrid contexts. These advantages have the potential to enhance battery longevity and promote the development of more sustainable energy systems.

Quantitative approach to Grover’s quantum walk on graphs

In this paper, we study Grover’s search algorithm focusing on continuous-time quantum walk on graphs. We propose an alternative optimization approach to Grover’s algorithm on graphs that can be summarized as follows: Instead of finding specific graph topologies convenient for the related quantum walk, we fix the graph topology and vary the underlying graph Laplacians. As a result, we search for the most appropriate analytical structure on graphs endowed with fixed topologies yielding better search outcomes. We discuss strategies to investigate the optimality of Grover’s algorithm and provide an example with an easy tunable graph Laplacian to investigate our ideas.

Joint energy beamforming and bandwidth allocation for cooperative localization in passive sensor networks

Passive sensor networks (PSNs) harvest continuous radio-frequency (RF) energy to work. However, the location information is difficult to maintain with unstable power supply for each passive sensor node. Cooperative localization is a promising solution utilizes the cooperation between the nodes with unknown positions to maintain the location information. And the localization performance relies on the attained energy from the energy access point (E-AP) and also the allocated bandwidth for each node. In this paper, we analyze the impacts of energy beamforming and bandwidth allocation on the cooperative localization performance of PSNs. We formulate the Fisher information matrix (FIM) and deduce the squared position error bound (SPEB) for the proposed network. Then, we propose an alternative optimization method for jointly energy beamforming and bandwidth allocation. Firstly, we employ the first-order Taylor expansions based on the trust region method to allocate the bandwidth of each node. Secondly, the Lagrangian multiplier based energy beamforming strategy is proposed. Then, the bandwidth allocation and energy beamforming calculations are executed alternatively and iteratively until the global optimal solution is achieved. The simulation results indicate that our proposed two-step method can achieve high precision positioning in different scenarios, and the SPEBs of proposed method reach 10−3 m2.

OPTIMASI WAKTU DAN BIAYA PADA PROYEK BENDUNGAN SELESAI (STUDI KASUS PADA PROYEK PEMBANGUNAN BENDUNGAN BENDO TAHAP 2)

The implementation of a project will not be separated from three factors, namely quality, time and cost. Time and cost factors can be calculated carefully and can be optimized. This needs to be done to evaluate the performance of a project, so that it becomes a lesson for the next project. This study aims to measure project performance, create optimal reschedules, optimal crash programs and calculate the time effectiveness and cost efficiency of rescheduling made in the Phase 2 Bendo Dam project. The method used in this study is the earned value and crash program with the data needed in this study is the Budget Plan (RAB), Time Schedule, Project Monthly Reports and Project Financial Reports. Then an analysis of the time and cost performance of the project is carried out, as well as calculating the optimization of alternatives for adding overtime hours and adding labor so that an optimal comparison of time and costs is obtained from the several alternatives. The results of the project performance using the earned value method in terms of costs are more accurate starting from the 18th month to the 25th month, as well as the completion time according to the planned time schedule. Meanwhile, the optimal rescheduling and crash program is adding workers with a cost efficiency of 0.66% of the total cost with a normal duration and time effectiveness of 88 calendar days.

Evaluating the efficiency of CCHP systems in Xinjiang Uygur Autonomous Region: An optimal strategy based on improved mother optimization algorithm

A novel approach is presented in this research to improve the design of a combined cooling, heating, and power (CCHP) system. The focus of the study is on a gas turbine system that can provide efficient power, cooling, and heating. The efficacy of the proposed technique is evaluated based on four parameters, namely energetic, exergetic, economic, and environmental features. To achieve superior and optimal results, an Improved Mother Optimization Algorithm (IMOA), a recently developed metaheuristic algorithm, is employed. This study provides a comprehensive guide for enhancing the performance, efficiency, and sustainability of CCHP systems. A practical case study was conducted to evaluate the proposed methodology in rural areas of Xinjiang Uygur Autonomous Region, China, over a period of one year. The simulation results reveal that the Gas Engine (GE) and the boiler components play a significant role in utilizing fuel energy, contributing 65 % and 35 %, respectively to the overall utilization. The analysis of the destruction rate of profile highlights that heat dissipation, mechanical losses, and electrical losses are the primary sources of energy losses within the system. When comparing the proposed IMOA with existing techniques, it is evident that the IMOA achieves a noteworthy 10 % reduction in energy consumption and a remarkable 15 % increase in overall efficiency of system. Furthermore, in terms of environmental impact, the IMOA leads to a substantial 20 % reduction in carbon dioxide (CO2) emissions compared to traditional optimization methods. The economic analysis results demonstrate that the IMOA-based approach not only improves cost-effectiveness by 25 %, but also yields an estimated return on investment (ROI) that is 18 % higher than alternative optimization techniques. Moreover, the proposed method with minimum fuel usage of 0.200 L/h, compared with Improved Butterfly Optimizer (IBO) with 0.201 L/h, Modified Mayfly (MM) algorithm with 0.205 L/h, Developed Owl Search (DOS) algorithm with 0.211 L/h, and Improved Owl Search (IOS) algorithm methods with 0.207 L/h, provides better results.

IRS Backscatter-Based Secrecy Enhancement against Active Eavesdropping

This paper proposes to combat active eavesdropping using intelligent reflecting surface (IRS) backscatter techniques. To be specific, the source (Alice) sends the confidential information to the intended user (Bob), while the eavesdropper (Willie) transmits a jamming signal to interrupt the transmission for more data interception. To enhance the secrecy, an IRS is deployed and connected with Alice through fiber to transform the jamming signal into the confidential signal by employing backscatter techniques. Based on the considered model, an optimization problem is formulated to maximize the signal-to-interference-plus-noise ratio (SINR) at Bob under the constraints of the transmit power at Alice, the reflection vector at the IRS, and the allowable maximum the SINR at Willie. To address the optimization problem, an alternate optimization algorithm is developed. The simulation results verify the achievable secrecy gain of the proposed scheme. The proposed scheme is effective in combating active eavesdropping. Furthermore, the deployment of large-scale IRS significantly enhances the secrecy rate.

Data‐driven decision‐focused surrogate modeling

AbstractWe introduce the concept of decision‐focused surrogate modeling for solving computationally challenging nonlinear optimization problems in real‐time settings. The proposed data‐driven framework seeks to learn a simpler, for example, convex, surrogate optimization model that is trained to minimize the decision prediction error, which is defined as the difference between the optimal solutions of the original and the surrogate optimization models. The learning problem, formulated as a bilevel program, can be viewed as a data‐driven inverse optimization problem to which we apply a decomposition‐based solution algorithm from previous work. We validate our framework through numerical experiments involving the optimization of common nonlinear chemical processes such as chemical reactors, heat exchanger networks, and material blending systems. We also present a detailed comparison of decision‐focused surrogate modeling with standard data‐driven surrogate modeling methods and demonstrate that our approach is significantly more data‐efficient while producing simple surrogate models with high decision prediction accuracy.