particle swarm optimisation algorithm
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Nutrients are essential to optimise plant growth. However, adding fertiliser changes the pH of the nutrition solution. This would impact plant growth as each plant types requires a specific pH range to thrive. Due to the nonlinearity characteristics, pH neutralisation adjustment is difficult but essential. In addition, alkaline solutions are not completely dissociated due to the presence of acid. For these reasons, a mathematical model to estimate the solution's pH would help improve the alkaline and acidic delivery accuracy. This study represents a pH water neutralisation behaviour using Particle Swarm Optimisation algorithm (PSO). The project begins with input and output data acquisition leading to the development of the PSO model. The model fit and residual distribution have also been analysed for this model. The model's performance was accepted based on a correlation test because the lag signal exceeded 95% of the confidence interval. The model also recorded a very minimal error, and this proved that a good agreement is established between the predicted and actual pH values.

Background: Due to the development of computing resources, machine learning techniques and models integrated with evolutionary algorithms have been successfully applied to solve a vast of engineering problems. With the advance in elastic metamaterial research, the identification of band structure, which reflects the physical property of Elastic Metamaterial, holds the key to the design of wave-controlled devices. Objective: In order to conduct bandgap analysis on two specific metamaterial structures, machine learning models that are integrated with the evolutionary algorithm are proposed to predict band structure. Methods: This paper proposes two integration models with a modified loss function for predicting elastic metamaterial’s band structure. The self-defined loss function composed of mean square error and concordance correlation is designed to ensure the numerical eigenfrequency values but also the position of each band. Results: The results of the integration models indicate the MLPs-PSO and RBFs – PSO models indeed have relatively satisfying performances on such pattern recognition tasks with respect to the numerical values of the error measurements. The performances of the machine learning models could be outstandingly improved by the Particle Swarm Optimisation algorithm. Conclusion: In short, the well-trained machine models are able to predict the band structure and could be contributing to bandgap enlargement study.

The particle swarm optimisation (PSO) algorithm has been widely used in hull form optimisation owing to its feasibility and fast convergence. However, similar to other intelligent algorithms, PSO also has the disadvantages of local premature convergence and low convergence performance. Moreover, optimization data are not used to analyse and reduce the range of values for relevant design variables. Our study aimed to solve these existing problems in the PSO algorithm and improve PSO from four aspects, namely data processing of particle swarm population initialisation, data processing of iterative optimisation, particle velocity adjustment, and particle cross-boundary configuration, in combination with space reduction technology. The improved PSO algorithm was used to optimise the hull form of an engineering vessel at Fn = 0.24 to reduce the wave-making resistance coefficient under static constraints. The results showed that the improved PSO algorithm could effectively improve the optimisation efficiency and reliability of PSO and effectively overcome the drawbacks of the PSO algorithm.

As the use of fossil fuels has led to global climate change due to global warming, most countries are aiming to reduce greenhouse gas emissions through the application of renewable energies. Due to the distributed and seasonal heating demand, the decarbonisation of heating is more challenging, especially for countries that are cold in winters. Electrically powered heat pumps are considered as an attractive solution for decarbonising heating sector. Since grid-powered heat pumps may significantly increase the power demand of the grid, this paper considers using local renewable energy to provide power for heat pumps, which is known as the grid independent renewable heating system including photovoltaic, wind turbine, battery storage system and thermal energy storage. This paper investigates a complete renewable heating system (RHS) framework and sizing the components to decarbonise building heating. The relationship between the reduction of gas consumption and the requirement of battery storage system (BSS) under the corresponding installation capacity of renewable components is analysed with their technical requirements. Then, according to different investment plans, this paper uses the particle swarm optimisation algorithm for optimal sizing of each component in the RHS to find a solution to minimise CO2 emissions. The results verify that the RHS with optimal sizing can minimise CO2 emissions and reduce the operational cost of natural gas. This work provides a feasible solution of how to invest the RHS to replace the existing heating system based on gas boilers and CHPs.

Purpose This study aims to propose optimised function-based evolutionary algorithms in this research to effectively replace the traditional electronic circuitry used in linearising constant temperature anemometer (CTA) and Microbridge mass flow sensor AWM 5000. Design/methodology/approach The proposed linearisation technique effectively uses the ratiometric function for the linearisation of CTA and Microbridge mass flow sensor AWM 5000. In addition, the well-known transfer relation, namely, the King’s Law is used for the linearisation of CTA and successfully implemented using LabVIEW 7.1. Findings Investigational results unveil that the proposed evolutionary optimised linearisation technique performs better in linearisation of both CTA and Mass flow sensors, and hence finds applications for computer-based flow measurement/control systems. Originality/value The evolutionary optimisation algorithms such as the real-coded genetic algorithm, particle swarm optimisation algorithm, differential evolution algorithm and covariance matrix adopted evolutionary strategy algorithm are used to determine the optimal values of the parameters present in the proposed ratiometric function. The performance measures, namely, the full-scale error and mean square error are used to analyse the overall performance of the proposed approach is compared to a state of art techniques available in the literature.

This paper presents an intelligent computational method using the PSO (particle swarm optimisation) algorithm to determine the optimum tilt angle of solar panels in PV systems. The objective of the paper is to assess the performance of this method against conventional methods of determining the optimum tilt angle. The method presented here can be used to determine the optimum tilt angle at any location around the world. In this paper, it was applied to Brunei Darussalam, and succeeded in computing monthly optimum tilt angles, ranging from 34.7ᵒ in December to -26.7ᵒ in September. Results showed that changing the tilt angle every month, as determined by the PSO algorithm, increased annual yield by: (i) 5.94%, compared to keeping it fixed at 0ᵒ, (ii) 8.65%, compared to Lunde’s method and (iii) 17.31%, compared to Duffie and Beckman’s method. Benchmark test functions were used to compare and evaluate the performance of the PSO algorithm with the artificial bee colony (ABC) algorithm, another metaheuristic algorithm. The tests revealed that the PSO algorithm outperformed the ABC algorithm, exhibiting lower root mean square error and standard deviation, better convergence to the global minimum, more accurate location of the global minimum, and faster execution times.