An alternative way of evolutionary multimodal optimization: density-based population initialization strategy

Xu, Peilan; Luo, Wenjian; Xu, Jiafei; Qiao, Yingying; Zhang, Jiajia; Gu, Naijie

doi:10.1016/j.swevo.2021.100971

Cited by 13 publications

(3 citation statements)

References 25 publications

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“…Ref. [47], [48] indicates that the quality of the initial population can improve the performance of population-based stochastic search algorithms. Therefore, we use layout based on Campo [24] to initialize the layout of the heliostat field to make the initialised population closer to the optimal solution.…”

Section: A Overall Problem-solving Frameworkmentioning

confidence: 99%

Heliostat Field Layout via Niching and Elite Competition Swarm Optimization

Zou,

Zhou,

2024

IEEE Access

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Confronted with the challenges posed by climate change and the ongoing energy transition, solar energy is one of the important new energy sources, and the tower solar power plant has become an innovative solution to promote clean energy development. The optimization of heliostat field layout constitutes a crucial aspect in enhancing the operational efficiency of a concentrated solar power tower plant. Currently, the optimization of heliostat field layout has garnered widespread attention. In this paper, we propose the swarm optimization algorithm with niching and elite competition called NECSO to solve the large-scale heliostat field layout optimization. First, aiming to increase diversity and heterogeneity within the population, we employ a random grouping strategy to partition the population into distinct sub-swarms. Then, we design a niching and elite competition mechanism to harmonize the performance of the exploration. The niching competition is carried out within any sub-swarm to enhance the explorability of particles. The elite competition occurs between the elites which select from each sub-swarm to improve the convergence of particles. Additionally, we develop a mathematical model for the optimization of heliostat field layout. This model employs currently advanced computational methods, facilitating prompt and precise calculation of the optical efficiency in the heliostat field layout. To evaluate the performance of NECSO, we design 15 practical cases of heliostat field layout with varying scales. And then, we conduct comparative experiments with eight currently mainstream and excellent algorithms. The results indicate that NECSO exhibits competitive performance in solving the heliostat field layout optimization, particularly in large-scale cases.

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Section: A Overall Problem-solving Frameworkmentioning

confidence: 99%

Heliostat Field Layout via Niching and Elite Competition Swarm Optimization

Zou,

Zhou,

2024

IEEE Access

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“…The proposed NSPSO is designed to deal with RMOPs such as the finding of LSF which can also be seen as a special multimodal optimization problem. Therefore four-NMMSO [31], RSCMSAESII [25], Multi_AMO [32] and DP-MSCC-ES [33]-are chosen as competitive algorithms for the comparative study. Notably, the RS-CMSA-ESII won the championship in the GECCO 2020 Competition on Niching Methods for Multimodal Optimization.…”

Section: Comparative Analysis Settingsmentioning

confidence: 99%

A Novel Sampling Method Based on Normal Search Particle Swarm Optimization for Active Learning Reliability Analysis

Yuan

et al. 2023

Applied Sciences

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In active learning reliability methods, an approximation of limit state function (LSF) with high precision is the key to accurately calculating the failure probability (Pf). However, existing sampling methods cannot guarantee that candidate samples can approach the LSF actively, which lowers the accuracy and stability of the results and causes excess computational effort. In this paper, a novel candidate samples-generating algorithm was proposed, by which a group of evenly distributed candidate points on the predicted LSF of performance function (either the real one or the surrogate model) could be obtained. In the proposed method, determination of LSF is considered as an optimization problem in which the absolute value of performance function was considered as objective function. After this, a normal search particle swarm optimization (NSPSO) was designed to deal with such problems, which consists of a normal search pattern and a multi-strategy framework that ensures the uniform distribution and diversity of the solution that intends to cover the optimal region. Four explicit performance functions and two engineering cases were employed to verify the effectiveness and accuracy of NSPSO sampling method. Four state-of-the-art multi-modal optimization algorithms were used as competitive methods. Analysis results show that the proposed method outperformed all competitive methods and can provide candidate samples that evenly distributed on the LSF.

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“…The results of the previous studies indicate that heuristic generation of the initial population enhances the capability of GA to provide optimum or near to optimum solutions. However, the generation of the entire population should not be done heuristically as it may result in a population having identical solutions with very little diversity [51]. Therefore, the best way is to use a mixed approach of heuristically seeding some populations with good solutions and allowing the random generation of remaining solutions.…”

Section: A) Genetic Algorithmmentioning

confidence: 99%

An optimized deployment plan of ambulances for trauma patients

2022

IJATEE

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Emergency medical service (EMS) control centres should focus on strategically deploying ambulances to enable trauma patients to receive better care. The work proposed here aims to find an optimal deployment plan of ambulances for the existing base stations using the genetic algorithm (GA) based optimization component. The GA has been modified by incorporating a new proportion-based population seeding method for initializing the population. Considering a set of assumptions, the authors have applied the new strategy for allocating an optimal count of ambulances to 28 base stations in Delhi. The working environment of EMS that includes stochastic requests, travel time, and dynamic traffic conditions has been taken into account, and the optimization strategy has been implemented in a MATLAB environment. With the proposed work, the authors have been able to reduce the average response time (ART) by 6.7%. The simulation result has also demonstrated a comparison between GA and particle swarm optimization (PSO). In addition, some numerical experiments are performed to conclude the impact of different attributes on the value of ART.

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An alternative way of evolutionary multimodal optimization: density-based population initialization strategy

Cited by 13 publications

References 25 publications

Heliostat Field Layout via Niching and Elite Competition Swarm Optimization

Heliostat Field Layout via Niching and Elite Competition Swarm Optimization

A Novel Sampling Method Based on Normal Search Particle Swarm Optimization for Active Learning Reliability Analysis

An optimized deployment plan of ambulances for trauma patients

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