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Other Important Optimization Algorithms
Published in Krishn Kumar Mishra, Nature-Inspired Algorithms, 2023
Nature-inspired algorithms belong to a specific category of metaheuristic algorithms in which a heuristic function is implemented by mapping some natural phenomenon. As a result, these algorithms are also known as nature-inspired algorithms. These algorithms are very popular these days due to their broad applicability and use. Another factor that motivates us to create nature-inspired algorithms is the complexity of the problems. Optimization problems are complex problems that necessitate a significant amount of computational effort and are prone to failure as the problem size grows. Nature provides efficient and effective solutions to many real-life problems. It can direct the search in the right direction and produce an optimal solution to the problem in a short period. Nature-inspired algorithms provide a very simple method for such complex problems, which can also be implemented in a very short time. These algorithms seek the global optimal solution to the problem while maintaining a balance between exploitation and exploration. The optimization problem is defined as finding the best possible/desirable solution. Many nature-inspired algorithms, such as genetic algorithms, particle swarm optimization, and ant colony optimization (ACO) algorithms, are simple to understand and used to solve various complex problems. Scientists are providing excellent solutions for cutting-edge applications due to the simplicity of these algorithms.
Exploiting the Flexibility Value of Virtual Power Plants through Market Participation in Smart Energy Communities
Published in Ehsan Heydarian-Forushani, Hassan Haes Alhelou, Seifeddine Ben Elghali, Virtual Power Plant Solution for Future Smart Energy Communities, 2023
Georgios Skaltsis, Stylianos Zikos, Elpiniki Makri, Christos Timplalexis, Dimosthenis Ioannidis, Dimitrios Tzovaras
Optimization and control algorithms: Optimization techniques are used as the core feature for the optimal operation and control of the VPPs. Various techniques have been used in power systems optimization, including mixed-integer programming (both linear and nonlinear), dynamic optimization and GAs. An important part in the formulation of the optimization problem is the definition of its constraints. Those constraints can be either imposed by the physical equipment of the grid, or defined by the user in order to perform specific use cases. Optimal operation of the VPP can be perceived in multiple ways, depending on which stakeholder's benefit is attempting to serve. The most common ways of operation, typically defined by the problem's objective function, usually include the maximization of the economic benefit, the increase of self-consumption and the provision of maximum support to the grid through ASs.
Direct computation of Variable Speed Pumps for water distribution system analysis
Published in Bogumil Ulanicki, Kalanithy Vairavamoorthy, David Butler, Peter L.M. Bounds, Fayyaz Ali Memon, Water Management Challenges in Global Change, 2020
E. Todini, M.E. Tryby, Z.Y. Wu, T.M. Walski
The optimization of operations in consumption or production energy systems has been investigated for some decades, The interest in this area is not only related to the complexity of the problem but also mainly by the economical benefits resulting from the best Solution applied. An optimization problem is a mathematical model in which the main goal is to minimize or maximize a quantity through an objective function constrained by certain restrictions. The optimization models can use several methods, which nowadays are becoming more efficient due to the computer technology evolution. In Firmino el al. (2006) an optimization model using linear programming was developed to improve the pumping stations energy costs in Brazil. The study revealed that the energy costs could be reduced by 15%.
Surrogate feasibility testing–cutting for single-objective robust optimization under interval uncertainty
Published in Engineering Optimization, 2023
Randall J. Kania, Shapour Azarm
For many applications, restricting problems to be convex is an apt assumption (Ben-Tal, El Ghaoui, and Nemirovski 2019), and especially for applications considering real-time decision making, such as in control problems (Calafiore 2010; Campi and Garatti 2008; Bernardini and Bemporad 2009; Calafiore and Campi 2005; Chamanbaz et al. 2016). For engineering design optimization, convexity cannot always be assumed, and more general methods must be employed (Rudnick-Cohen, Herrmann, and Azarm 2020; Du and Chen 1999). Considering non-convexities will naturally lead to solving more difficult and computationally intensive optimization problems. To bring the computational cost within a more affordable range, algorithms such as the modified Bender’s decomposition (Siddiqui, Azarm, and Gabriel 2011) can be used, or the problem can be approximated by surrogates (Forrester, Sobester, and Keane 2008). The modified Bender’s decomposition (Siddiqui, Azarm, and Gabriel 2011) assumes a discretized range for the uncertain parameters. This can lead to infeasibilities in the solution if any of the constraints are violated at a value of the uncertain parameters not included in the discretization. A worst-case search is not susceptible to this, but it can be computationally costly. Therefore, surrogate modelling with the worst-case search is used in the method proposed in this article.
Surrogate-based design optimization of a centrifugal pump impeller
Published in Engineering Optimization, 2022
Ashutosh Kumar Jaiswal, Md. Hamid Siddique, Akshoy Ranjan Paul, Abdus Samad
Here, the main objective function y(x) (cost) and the state function gi(x) are determined using computationally expensive CFD simulations. By tradition, the optimization problems are solved either by a gradient-free algorithm, e.g. the genetic algorithm, or by a gradient-based algorithm. In this study, when the best surrogate model was built, the MOGA was used to find the optimum solution (Han and Zhang 2012). Here, the basic objective is to conserve energy by minimizing the electric power consumed by the centrifugal pumps in terms of shaft power. Therefore, the objective function was obtained for minimizing input power using the surrogate model. The objective function was defined for three geometric variables related to blade exit angles, with ranges selected based on a literature survey. Thereafter, some sample designs were generated by the LHS method. The results obtained through sample designs were used to construct the objective function through the best surrogate model. The best surrogate model was selected by checking the cross-validation error of the objective function obtained through CFD simulation using different surrogate models; namely, KRG, RBF, RSA and weighted average (WTA) surrogate model. Finally, the optimized design point was obtained using the MOGA.
Characterization of electromagnetic parameters through inversion using metaheuristic technique
Published in Inverse Problems in Science and Engineering, 2021
Mohamed Elkattan, Aladin Kamel
Several approaches can be used to solve an optimization problem such as Genetic algorithm, and Neural Network, etc. While Genetic algorithm is computationally expensive, and Neural Network requires a huge set of training examples to find an optimum solution, the simulated annealing method has the advantage of being flexible with respect to the evolutions of the problem and easy to implement. The technique of simulated annealing is based on the physical analogy of cooling crystals that, when cooled very slowly, attempt to arrive at its globally minimal potential energy [36]. Simulated annealing interprets slow cooling as a slow decrease in the probability of accepting worse solutions as it explores the solution space. Accepting worse solutions is a fundamental property of the technique because it allows for a more extensive search for the optimal solution through the ability to escape local minima [37].