Comparative methods for optimal power flow solution in distribution networks considering distributed generators : metaheuristics vs convex optimization

Este artículo presenta un análisis de diferentes metodologías de optimización para realizar una comparación objetiva entre la optimización metaheurística y la optimización convexa en redes de distribución, mediante la inclusión de generación distribuida en las mismas, la herramienta utilizada para la implementación y obtención de resultados es el software Matlab. El objetivo es determinar el tamaño óptimo de las GD a integrar en las redes, para reducir las pérdidas de potencia activa (función objetivo) de las redes de distribución.This article presents an analysis of different optimization methodologies in order to make an objective comparison between metaheuristic optimization and convex optimization in distribution networks, through the inclusion of distributed generation in them, the tool used for implementation and obtaining results is Matlab software. The objective is to determine the optimal size of the DGs to be integrated into the networks, in order to reduce the active power losses (objective function) of the distribution networks

Optimization of Slope Critical Surfaces Considering Seepage and Seismic Effects Using Finite Element Method and Five Meta-Heuristic Algorithms

One of the most crucial geotechnical engineering problems is the stability of slopes that are still attracting scientists and engineers. In this study, five recently developed meta-heuristic methods are utilized to determine the Critical Failure Surface (CFS) and its corresponding Factor of Safety (FOS). Through the FOS calculations, the Finite Element Method (FEM) is employed to convert the strong form of the main differential equation to a weak form. Additional to the general loading, seismic forces and seepage effect are considered, as well. Finally, the proposed optimization procedure is applied to numerical benchmark examples, and results are compared with other methods

Sine Cosine Algorithm for Optimization

This open access book serves as a compact source of information on sine cosine algorithm (SCA) and a foundation for developing and advancing SCA and its applications. SCA is an easy, user-friendly, and strong candidate in the field of metaheuristics algorithms. Despite being a relatively new metaheuristic algorithm, it has achieved widespread acceptance among researchers due to its easy implementation and robust optimization capabilities. Its effectiveness and advantages have been demonstrated in various applications ranging from machine learning, engineering design, and wireless sensor network to environmental modeling. The book provides a comprehensive account of the SCA, including details of the underlying ideas, the modified versions, various applications, and a working MATLAB code for the basic SCA

Métodos comparativos para la solución óptima del flujo de energía en redes de distribución considerando generadores distribuidos: metaheurística vs. optimización convexa

Objective: This article presents an analysis of different optimization methodologies, which aims to make an objective comparison between metaheuristic and convex optimization methods in distribution networks, focusing on the inclusion of distributed generation (DG). The MATLAB software is used as a tool for implementation and obtaining results. The objective was to determine the optimal size of the DGs to be integrated into the networks, with the purpose of reducing the active power losses (objective function). Methodology: Based on the specialized literature, the methodologies are selected, and the bases and conditions for the implementation of the optimization techniques are determined. In the case of second-order cone programming (SOCP), the relaxation of the nonlinear optimal power flow (OPF) problem is performed in order to use convex optimization. Then, the structures of each technique are established and applied in the MATLAB software. Due to the iterative nature of metaheuristic methods, the data corresponding to 100 compilations for each algorithm are collected. Finally, by means of a statistical analysis, the optimal solutions for the objective function in each methodology are determined, and, with these results, the different methods applied to the networks are compared. Results: By analyzing 33- and 69-node systems, it is demonstrated that metaheuristic methods are able to effectively size DGs in distribution systems and yield good results that are similar and comparable to SOCP regarding the OPF problem. Genetic algorithms (GA) showed the best results for the studied implementation, even surpassing the SOCP. Conclusions: Metaheuristic methods proved to be algorithms with a high computational efficiency and are suitable for real-time applications if implemented in distribution systems with well-defined conditions. These techniques provide innovative ideas because they are not rigid algorithms, which makes them very versatile methods that can be adapted to any combinatorial optimization problem and software, yielding results even at the convex optimization level.Objetivo: Este artículo presenta un análisis de diferentes metodologías de optimización, cuyo fin es realizar una comparación objetiva entre métodos de optimización metaheurística y convexa en redes de distribución con énfasis en la inclusión de generación distribuida (DG). Se utiliza el software MATLAB como herramienta para la implementación y la obtención de resultados. El objetivo es determinar el tamaño óptimo de las DG a integrar en las redes, con el fin de reducir las pérdidas de potencia activa (función objetivo). Metodología: A partir de la literatura especializada, se seleccionan las metodologías y se determinan las bases y condiciones para la implementación de las técnicas de optimización. En el caso de la programación cónica de segundo orden (SOCP), se realiza la relajación del problema de flujo de potencia óptimo (OPF) no lineal para utilizar optimización convexa. Luego, las estructuras de cada técnica se establecen y aplican en el software MATLAB. Debido al carácter iterativo de los métodos metaheurísticos, se recolectan los datos correspondientes a 100 compilaciones para cada algoritmo. Finalmente, mediante un análisis estadístico, se determinan las soluciones óptimas para la función objetivo en cada metodología y, con estos resultados, se comparan los diferentes métodos aplicados a las redes. Resultados: A partir del análisis de sistemas de 33 y 69 nodos, se demuestra que los métodos metaheurísticos son capaces de dimensionar DGs manera efectiva en sistemas de distribución y dan buenos resultados, similares y comparables a la SOCP en el problema OPF. El algoritmo genético (GA) mostró los mejores resultados para la implementación realizada, superando incluso a la SOCP. Conclusiones: Los métodos metaheurísticos demostraron ser algoritmos de alta eficiencia computacional y son adecuados para aplicaciones en tiempo real si se implementan en sistemas de distribución con condiciones correctamente definidas. Estas técnicas aportan ideas innovadoras porque no son algoritmos rígidos, lo que las convierte en métodos muy versátiles que pueden adaptarse a cualquier problema de optimización combinatoria y a cualquier software, dando resultados incluso a nivel de optimización convexa

Lotus effect optimization algorithm (LEA): a lotus nature-inspired algorithm for engineering design optimization

Here we introduce a new evolutionary algorithm called the Lotus Effect Algorithm, which combines efficient operators from the dragonfly algorithm, such as the movement of dragonflies in flower pollination for exploration, with the self-cleaning feature of water on flower leaves known as the lotus effect, for extraction and local search operations. The authors compared this method to other improved versions of the dragonfly algorithm using standard benchmark functions, and it outperformed all other methods according to Fredman\u27s test on 29 benchmark functions. The article also highlights the practical application of LEA in reducing energy consumption in IoT nodes through clustering, resulting in increased packet delivery ratio and network lifetime. Additionally, the performance of the proposed method was tested on real-world problems with multiple constraints, such as the welded beam design optimization problem and the speed-reducer problem applied in a gearbox, and the results showed that LEA performs better than other methods in terms of accuracy

Evolvability signatures of generative encodings: beyond standard performance benchmarks

Evolutionary robotics is a promising approach to autonomously synthesize machines with abilities that resemble those of animals, but the field suffers from a lack of strong foundations. In particular, evolutionary systems are currently assessed solely by the fitness score their evolved artifacts can achieve for a specific task, whereas such fitness-based comparisons provide limited insights about how the same system would evaluate on different tasks, and its adaptive capabilities to respond to changes in fitness (e.g., from damages to the machine, or in new situations). To counter these limitations, we introduce the concept of "evolvability signatures", which picture the post-mutation statistical distribution of both behavior diversity (how different are the robot behaviors after a mutation?) and fitness values (how different is the fitness after a mutation?). We tested the relevance of this concept by evolving controllers for hexapod robot locomotion using five different genotype-to-phenotype mappings (direct encoding, generative encoding of open-loop and closed-loop central pattern generators, generative encoding of neural networks, and single-unit pattern generators (SUPG)). We observed a predictive relationship between the evolvability signature of each encoding and the number of generations required by hexapods to adapt from incurred damages. Our study also reveals that, across the five investigated encodings, the SUPG scheme achieved the best evolvability signature, and was always foremost in recovering an effective gait following robot damages. Overall, our evolvability signatures neatly complement existing task-performance benchmarks, and pave the way for stronger foundations for research in evolutionary robotics.Comment: 24 pages with 12 figures in the main text, and 4 supplementary figures. Accepted at Information Sciences journal (in press). Supplemental videos are available online at, see http://goo.gl/uyY1R

Acquiring moving skills in robots with evolvable morphologies: Recent results and outlook

© 2017 ACM. We construct and investigate a strongly embodied evolutionary system, where not only the controllers but also the morphologies undergo evolution in an on-line fashion. In these studies, we have been using various types of robot morphologies and controller architectures in combination with several learning algorithms, e.g. evolutionary algorithms, reinforcement learning, simulated annealing, and HyperNEAT. This hands-on experience provides insights and helps us elaborate on interesting research directions for future development

Joint Optimization of Deployment and Trajectory in UAV and IRS-Assisted IoT Data Collection System

Unmanned aerial vehicles (UAVs) can be applied in many Internet of Things (IoT) systems, e.g., smart farms, as a data collection platform. However, the UAV-IoT wireless channels may be occasionally blocked by trees or high-rise buildings. An intelligent reflecting surface (IRS) can be applied to improve the wireless channel quality by smartly reflecting the signal via a large number of low-cost passive reflective elements. This article aims to minimize the energy consumption of the system by jointly optimizing the deployment and trajectory of the UAV. The problem is formulated as a mixed-integer-and-nonlinear programming (MINLP), which is challenging to address by the traditional solution, because the solution may easily fall into the local optimal. To address this issue, we propose a joint optimization framework of deployment and trajectory (JOLT), where an adaptive whale optimization algorithm (AWOA) is applied to optimize the deployment of the UAV, and an elastic ring self-organizing map (ERSOM) is introduced to optimize the trajectory of the UAV. Specifically, in AWOA, a variable-length population strategy is applied to find the optimal number of stop points, and a nonlinear parameter a and a partial mutation rule are introduced to balance the exploration and exploitation. In ERSOM, a competitive neural network is also introduced to learn the trajectory of the UAV by competitive learning, and a ring structure is presented to avoid the trajectory intersection. Extensive experiments are carried out to show the effectiveness of the proposed JOLT framework.Comment: 11 pages, 7 figures, 4 table

Spatial-planning-based ecosystem adaptation (spbea): a concept and modeling of prone shoreline retreat areas

The fitness-dependent optimizer (FDO), a newly proposed swarm intelligent algorithm, is focused on the reproductive mechanism of bee swarming and collective decision-making. To optimize the performance, FDO calculates velocity (pace) differently. FDO calculates weight using the fitness function values to update the search agent position during the exploration and exploitation phases. However, the FDO encounters slow convergence and unbalanced exploitation and exploration. Hence, this study proposes a novel hybrid of the sine cosine algorithm and fitness-dependent optimizer (SC-FDO) for updating the velocity (pace) using the sine cosine scheme. This proposed algorithm, SC-FDO, has been tested over 19 classical and 10 IEEE Congress of Evolutionary Computation (CEC-C06 2019) benchmark test functions. The findings revealed that SC-FDO achieved better performances in most cases than the original FDO and well-known optimization algorithms. The proposed SC-FDO improved the original FDO by achieving a better exploit-explore tradeoff with a faster convergence speed. The SC-FDO was applied to the missing data estimation cases and refined the missingness as optimization problems. This is the first time, to our knowledge, that nature-inspired algorithms have been considered for handling time series datasets with low and high missingness problems (10%-90%). The impacts of missing data on the predictive ability of the proposed SC-FDO were evaluated using a large weather dataset from 1985 until 2020. The results revealed that the imputation sensitivity depends on the percentages of missingness and the imputation models. The findings demonstrated that the SC-FDO based multilayer perceptron (MLP) trainer outperformed the other three optimizer trainers with the highest average accuracy of 90% when treating the high-low missingness in the dataset

Applied (Meta)-Heuristic in Intelligent Systems

Engineering and business problems are becoming increasingly difficult to solve due to the new economics triggered by big data, artificial intelligence, and the internet of things. Exact algorithms and heuristics are insufficient for solving such large and unstructured problems; instead, metaheuristic algorithms have emerged as the prevailing methods. A generic metaheuristic framework guides the course of search trajectories beyond local optimality, thus overcoming the limitations of traditional computation methods. The application of modern metaheuristics ranges from unmanned aerial and ground surface vehicles, unmanned factories, resource-constrained production, and humanoids to green logistics, renewable energy, circular economy, agricultural technology, environmental protection, finance technology, and the entertainment industry. This Special Issue presents high-quality papers proposing modern metaheuristics in intelligent systems