Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the `Rush to Heuristics'

In the power and energy systems area, a progressive increase of literature contributions that contain applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming that the proposed method is better than other methods that are based on weak comparisons. This ‘rush to heuristics’ does not happen in the evolutionary computation domain, where the rules for setting up rigorous comparisons are stricter but are typical of the domains of application of the metaheuristics. This paper considers the applications to power and energy systems and aims at providing a comprehensive view of the main issues that concern the use of metaheuristics for global optimization problems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed. Some weaknesses and pitfalls that are found in literature contributions are identified, and specific guidelines are provided regarding how to prepare sound contributions on the application of metaheuristic algorithms to specific problems

Metaheuristic optimization of power and energy systems: underlying principles and main issues of the 'rush to heuristics'

In the power and energy systems area, a progressive increase of literature contributions containing applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming that the proposed method is better than other methods based on weak comparisons. This 'rush to heuristics' does not happen in the evolutionary computation domain, where the rules for setting up rigorous comparisons are stricter, but are typical of the domains of application of the metaheuristics. This paper considers the applications to power and energy systems, and aims at providing a comprehensive view of the main issues concerning the use of metaheuristics for global optimization problems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed. Some weaknesses and pitfalls found in literature contributions are identified, and specific guidelines are provided on how to prepare sound contributions on the application of metaheuristic algorithms to specific problems

Intelligent Breast Cancer Diagnosis with Heuristic-assisted Trans-Res-U-Net and Multiscale DenseNet using Mammogram Images

Breast cancer (BC) significantly contributes to cancer-related mortality in women, underscoring the criticality of early detection for optimal patient outcomes. A mammography is a key tool for identifying and diagnosing breast abnormalities; however, accurately distinguishing malignant mass lesions remains challenging. To address this issue, we propose a novel deep learning approach for BC screening utilizing mammography images. Our proposed model comprises three distinct stages: data collection from established benchmark sources, image segmentation employing an Atrous Convolution-based Attentive and Adaptive Trans-Res-UNet (ACA-ATRUNet) architecture, and BC identification via an Atrous Convolution-based Attentive and Adaptive Multi-scale DenseNet (ACA-AMDN) model. The hyperparameters within the ACA-ATRUNet and ACA-AMDN models are optimised using the Modified Mussel Length-based Eurasian Oystercatcher Optimization (MML-EOO) algorithm. Performance evaluation, leveraging multiple metrics, is conducted, and a comparative analysis against conventional methods is presented. Our experimental findings reveal that the proposed BC detection framework attains superior precision rates in early disease detection, demonstrating its potential to enhance mammography-based screening methodologies.Comment: 22 pages, 17 figures, 4 Tables and Appendix A: Supplementary Materia

Rethinking solar photovoltaic parameter estimation: global optimality analysis and a simple efficient differential evolution method

Accurate, fast, and reliable parameter estimation is crucial for modeling, control, and optimization of solar photovoltaic (PV) systems. In this paper, we focus on the two most widely used benchmark datasets and try to answer (i) whether the global minimum in terms of root mean square error (RMSE) has already been reached; and (ii) whether a significantly simpler metaheuristic, in contrast to currently sophisticated ones, is capable of identifying PV parameters with comparable performance, e.g., attaining the same RMSE. We address the former using an interval analysis based branch and bound algorithm and certify the global minimum rigorously for the single diode model (SDM) as well as locating a fairly tight upper bound for the double diode model (DDM) on both datasets. These obtained values will serve as useful references for metaheuristic methods, since none of them can guarantee or recognize the global minimum even if they have literally discovered it. However, this algorithm is excessively slow and unsuitable for time-sensitive applications (despite the great insights on RMSE that it yields). Regarding the second question, extensive examination and comparison reveal that, perhaps surprisingly, a classic and remarkably simple differential evolution (DE) algorithm can consistently achieve the certified global minimum for the SDM and obtain the best known result for the DDM on both datasets. Thanks to its extreme simplicity, the DE algorithm takes only a fraction of the running time required by other contemporary metaheuristics and is thus preferable in real-time scenarios. This unusual (and certainly notable) finding also indicates that the employment of increasingly complicated metaheuristics might possibly be somewhat overkill for regular PV parameter estimation. Finally, we discuss the implications of these results and suggest promising directions for future development.Comment: v2, see source code at https://github.com/ShuhuaGao/rePVes

Project schedule optimisation utilising genetic algorithms

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis extends the body of research into the application of Genetic Algorithms to the Project Scheduling Problem (PSP). A thorough literature review is conducted in this area as well as in the application of other similar meta-heuristics. The review extends previous similar reviews to include PSP utilizing the Design Structure Matrix (DSM), as well as incorporating recent developments. There is a need within industry for optimisation algorithms that can assist in the identification of optimal schedules when presented with a network that can present a number of possible alternatives. The optimisation requirement may be subtle only performing slight resource levelling or more profound by selecting an optimal mode of execution for a number of activities or evaluating a number of alternative strategies. This research proposes a unique, efficient algorithm using adaptation based on the fitness improvement over successive generations. The algorithm is tested initially using a MATLAB based implementation to solve instances of the travelling salesman problem (TSP). The algorithm is then further developed both within MATLAB and Microsoft Project Visual Basic to optimise both known versions of the Resource Constrained Project Scheduling Problems as well as investigating newly defined variants of the problem class

Comparison of High Performance Parallel Implementations of TLBO and Jaya Optimization Methods on Manycore GPU

The utilization of optimization algorithms within engineering problems has had a major rise in recent years, which has led to the proliferation of a large number of new algorithms to solve optimization problems. In addition, the emergence of new parallelization techniques applicable to these algorithms to improve their convergence time has made it a subject of study by many authors. Recently, two optimization algorithms have been developed: Teaching-Learning Based Optimization and Jaya. One of the main advantages of both algorithms over other optimization methods is that the former do not need to adjust specific parameters for the particular problem to which they are applied. In this paper, the parallel implementations of Teaching-Learning Based Optimization and Jaya are compared. The parallelization of both algorithms is performed using manycore GPU techniques. Different scenarios will be created involving functions frequently applied to the evaluation of optimization algorithms. Results will make it possible to compare both parallel algorithms with regard to the number of iterations and the time needed to perform them so as to obtain a predefined error level. The GPU resources occupation in each case will also be analyzed.This work was supported in part by the Spanish Ministry of Economy and Competitiveness under Grant TIN2017-89266-R, in part by FEDER funds (MINECO/FEDER/UE), and in part by the Spanish Ministry of Science, Innovation, and Universities co-financed by FEDER funds under Grant RTI2018-098156-B-C54

Metaheuristics for Traffic Control and Optimization: Current Challenges and Prospects

Intelligent traffic control at signalized intersections in urban areas is vital for mitigating congestion and ensuring sustainable traffic operations. Poor traffic management at road intersections may lead to numerous issues such as increased fuel consumption, high emissions, low travel speeds, excessive delays, and vehicular stops. The methods employed for traffic signal control play a crucial role in evaluating the quality of traffic operations. Existing literature is abundant, with studies focusing on applying regression and probability-based methods for traffic light control. However, these methods have several shortcomings and can not be relied on for heterogeneous traffic conditions in complex urban networks. With rapid advances in communication and information technologies in recent years, various metaheuristics-based techniques have emerged on the horizon of signal control optimization for real-time intelligent traffic management. This study critically reviews the latest advancements in swarm intelligence and evolutionary techniques applied to traffic control and optimization in urban networks. The surveyed literature is classified according to the nature of the metaheuristic used, considered optimization objectives, and signal control parameters. The pros and cons of each method are also highlighted. The study provides current challenges, prospects, and outlook for future research based on gaps identified through a comprehensive literature review

Differential Cloud Particles Evolution Algorithm Based on Data-Driven Mechanism for Applications of ANN

Computational scientists have designed many useful algorithms by exploring a biological process or imitating natural evolution. These algorithms can be used to solve engineering optimization problems. Inspired by the change of matter state, we proposed a novel optimization algorithm called differential cloud particles evolution algorithm based on data-driven mechanism (CPDD). In the proposed algorithm, the optimization process is divided into two stages, namely, fluid stage and solid stage. The algorithm carries out the strategy of integrating global exploration with local exploitation in fluid stage. Furthermore, local exploitation is carried out mainly in solid stage. The quality of the solution and the efficiency of the search are influenced greatly by the control parameters. Therefore, the data-driven mechanism is designed for obtaining better control parameters to ensure good performance on numerical benchmark problems. In order to verify the effectiveness of CPDD, numerical experiments are carried out on all the CEC2014 contest benchmark functions. Finally, two application problems of artificial neural network are examined. The experimental results show that CPDD is competitive with respect to other eight state-of-the-art intelligent optimization algorithms

Project schedule optimisation utilising genetic algorithms

This thesis extends the body of research into the application of Genetic Algorithms to the Project Scheduling Problem (PSP). A thorough literature review is conducted in this area as well as in the application of other similar meta-heuristics. The review extends previous similar reviews to include PSP utilizing the Design Structure Matrix (DSM), as well as incorporating recent developments. There is a need within industry for optimisation algorithms that can assist in the identification of optimal schedules when presented with a network that can present a number of possible alternatives. The optimisation requirement may be subtle only performing slight resource levelling or more profound by selecting an optimal mode of execution for a number of activities or evaluating a number of alternative strategies. This research proposes a unique, efficient algorithm using adaptation based on the fitness improvement over successive generations. The algorithm is tested initially using a MATLAB based implementation to solve instances of the travelling salesman problem (TSP). The algorithm is then further developed both within MATLAB and Microsoft Project Visual Basic to optimise both known versions of the Resource Constrained Project Scheduling Problems as well as investigating newly defined variants of the problem class.EThOS - Electronic Theses Online ServiceGBUnited Kingdo