MORSA: Multi-objective reptile search algorithm based on elite non-dominated sorting and grid indexing mechanism for wind farm layout optimization problem

Zheng, Yue; Wang, Jie-Sheng; Zhu, Jun-Hua; Zhang, Xin-Yue; Xing, Yu-Xuan; Zhang, Yun-Hao

doi:10.1016/j.energy.2024.130771

Energy

2024

DOI: 10.1016/j.energy.2024.130771

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MORSA: Multi-objective reptile search algorithm based on elite non-dominated sorting and grid indexing mechanism for wind farm layout optimization problem

Yue Zheng,

Jie-Sheng Wang,

Jun-Hua Zhu

et al.

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Cited by 3 publications

References 44 publications

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Multi-scale offshore wind farm site selection decision framework based on GIS, MCDM and meta-heuristic algorithm

Shao,

Mao,

Sun

et al. 2025

Ocean Engineering

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Multi-scale offshore wind farm site selection decision framework based on GIS, MCDM and meta-heuristic algorithm

Shao,

Mao,

Sun

et al. 2025

Ocean Engineering

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Wind Farm Layout Optimization/Expansion of Real Wind Turbines with a Parallel Collaborative Multi-Objective Optimization Algorithm

Bouchekara,

Ramli,

Javaid

2024

Energies

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The objective of this paper is to study the Wind Farm Layout Optimization/expansion problem. This problem is formulated here as a Multi-Objective Optimization Problem considering the total power output and net efficiency of Wind Farms as objectives along with specific constraints. Once formulated, this problem needs to be solved efficiently. For that, a new approach based on a combination of five Multi-Objective Optimization algorithms, which is named the Parallel Collaborative Multi-Objective Optimization Algorithm, is developed and implemented. This technique is checked on seven test cases; for each case, the goal is to find a set of optimal solutions called the Pareto Front, which can be exploited later. The acquired solutions were compared with other approaches and the proposed approach was found to be the better one. Finally, this work concludes that the proposed approach gives, in a single run, a set of optimal solutions from which a designer/planner can select the best layout of a designed Wind Farm using expertise and applying technical and economic constraints.

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DTSA: Dynamic Tree-Seed Algorithm with Velocity-Driven Seed Generation and Count-Based Adaptive Strategies

Jiang,

Huang,

et al. 2024

Symmetry

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The Tree-Seed Algorithm (TSA) has been effective in addressing a multitude of optimization issues. However, it has faced challenges with early convergence and difficulties in managing high-dimensional, intricate optimization problems. To tackle these shortcomings, this paper introduces a TSA variant (DTSA). DTSA incorporates a suite of methodological enhancements that significantly bolster TSA’s capabilities. It introduces the PSO-inspired seed generation mechanism, which draws inspiration from Particle Swarm Optimization (PSO) to integrate velocity vectors, thereby enhancing the algorithm’s ability to explore and exploit solution spaces. Moreover, DTSA’s adaptive velocity adaptation mechanism based on count parameters employs a counter to dynamically adjust these velocity vectors, effectively curbing the risk of premature convergence and strategically reversing vectors to evade local optima. DTSA also integrates the trees population integrated evolutionary strategy, which leverages arithmetic crossover and natural selection to bolster population diversity, accelerate convergence, and improve solution accuracy. Through experimental validation on the IEEE CEC 2014 benchmark functions, DTSA has demonstrated its enhanced performance, outperforming recent TSA variants like STSA, EST-TSA, fb-TSA, and MTSA, as well as established benchmark algorithms such as GWO, PSO, BOA, GA, and RSA. In addition, the study analyzed the best value, mean, and standard deviation to demonstrate the algorithm’s efficiency and stability in handling complex optimization issues, and DTSA’s robustness and efficiency are proven through its successful application in five complex, constrained engineering scenarios, demonstrating its superiority over the traditional TSA by dynamically optimizing solutions and overcoming inherent limitations.

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MORSA: Multi-objective reptile search algorithm based on elite non-dominated sorting and grid indexing mechanism for wind farm layout optimization problem

Cited by 3 publications

References 44 publications

Multi-scale offshore wind farm site selection decision framework based on GIS, MCDM and meta-heuristic algorithm

Multi-scale offshore wind farm site selection decision framework based on GIS, MCDM and meta-heuristic algorithm

Wind Farm Layout Optimization/Expansion of Real Wind Turbines with a Parallel Collaborative Multi-Objective Optimization Algorithm

DTSA: Dynamic Tree-Seed Algorithm with Velocity-Driven Seed Generation and Count-Based Adaptive Strategies

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