Two New Bio-inspired Particle Swarm Optimisation Algorithms for Single-Objective Continuous Variable Problems Based on Eavesdropping and Altruistic Animal Behaviours DOI Open Access
Fevzi Tugrul Varna, Phil Husbands

Published: July 3, 2024

This paper presents two novel bio-inspired particle swarm optimisation (PSO) variants: biased eavesdropping PSO (BEPSO) and altruistic heterogeneous (AHPSO). These algorithms are inspired by types of group behaviour found in nature that have not previously been exploited search algorithms. The primary the BEPSO algorithm is observed coupled with a cognitive bias mechanism enables particles to make decisions on cooperation. second algorithm, AHPSO, conceptualises as energy-driven agents which allows formation lending-borrowing relationships. mechanisms underlying these provide new approaches maintaining diversity contributes preventing premature convergence. were tested 30, 50 100-dimensional CEC'13, CEC'14 CEC'17 test suites, various constrained real-world problems, against 13 well-known variants CEC competition winner, differential evolution L-SHADE. experimental results show both algorithms, very competitive performance unconstrained suites problems. They significantly better than most other variant problem sets no comparator was either them any 100-d sets.

Language: Английский

Quantum maximum power point tracking (QMPPT) for optimal solar energy extraction DOI Creative Commons

Habib Feraoun,

Mehdi Fazilat,

Reda Dermouche

et al.

Systems and Soft Computing, Journal Year: 2024, Volume and Issue: 6, P. 200118 - 200118

Published: July 4, 2024

Solar energy is key to achieving a more environmentally responsible future. One way exploit it use semiconductor technology through solar panels generate clean, sustainable, and controllable energy. However, the of such solutions must be optimised by methods as maximum power point tracking (MPPT) extract available Although MPPT algorithms have been widely used improved, newer approaches, quantum computing, appears hold promise new performance levels, particularly for real-time implementation. The goal this work develop test algorithm photovoltaic (PV) problem using particle swarm optimisation. classic was evaluated under three main operating conditions: normal, high-temperature, partial shading conditions. This represents variety environmental scenarios that can affect efficiency generation. According study's results, classical recorded 0.15% than in normal conditions, generated 3.33% higher temperature tests 0.89% test. Moreover, lower duty cycles tests. While may slight edge output operation indicates superior challenging conditions consistently reveals promising overall efficiency.

Language: Английский

Citations

3
Two New Bio-Inspired Particle Swarm Optimisation Algorithms for Single-Objective Continuous Variable Problems Based on Eavesdropping and Altruistic Animal Behaviours DOI Creative Commons
Fevzi Tugrul Varna, Phil Husbands

Biomimetics, Journal Year: 2024, Volume and Issue: 9(9), P. 538 - 538

Published: Sept. 5, 2024

This paper presents two novel bio-inspired particle swarm optimisation (PSO) variants, namely biased eavesdropping PSO (BEPSO) and altruistic heterogeneous (AHPSO). These algorithms are inspired by types of group behaviour found in nature that have not previously been exploited search algorithms. The primary the BEPSO algorithm is observed coupled with a cognitive bias mechanism enables particles to make decisions on cooperation. second algorithm, AHPSO, conceptualises as energy-driven agents behaviour, which allows for formation lending-borrowing relationships. mechanisms underlying these provide new approaches maintaining diversity, contributes prevention premature convergence. were tested 30, 50 100-dimensional CEC'13, CEC'14 CEC'17 test suites various constrained real-world problems, well against 13 well-known CEC competition winner, differential evolution L-SHADE recent I-CPA metaheuristic. experimental results show both AHPSO very competitive performance unconstrained problems. On CEC13 suite, across all dimensions, performed statistically significantly better than 10 15 comparator algorithms, while none remaining 5 either or AHPSO. CEC17 50D 100D 11 4 problem set, terms mean rank 30 runs was first, third.

Language: Английский

Citations

2
Photovoltaic Maximum Power Point Tracking Technology Based on Improved Perturbation Observation Method and Backstepping Algorithm DOI Open Access
Yulin Wang, Li-Ying Sun

Electronics, Journal Year: 2024, Volume and Issue: 13(19), P. 3960 - 3960

Published: Oct. 8, 2024

Photovoltaic power generation systems mainly use the maximum tracking (MPPT) controller to adjust voltage and current of solar cells in photovoltaic array, so that array runs at point (MPP) achieve purpose output. At present, stations adopt traditional method track point, but this fixed step easily causes output oscillation when it falls into local extreme under partial shadow conditions. In order solve these problems, paper proposes an improved perturbation observation backstepping (IP&O-backstepping) replace applied MPPT optimize operating state cell, thereby improving increasing array. The algorithm first uses (IP&O) search reference voltage. Secondly, is input for tracking. Finally, tracks makes operate point. simulation carried out by using MATLAB/Simulink. IP&O-backstepping compared with intelligent method, results show above algorithm, can not only also has a faster speed, almost no

Language: Английский

Citations

1
Experimental Validation of a Novel Hybrid Equilibrium Slime Mould Optimization for Solar Photovoltaic System DOI Creative Commons
Djallal Eddine Zabia, Hamza Afghoul, Okba Kraa

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(20), P. e38943 - e38943

Published: Oct. 1, 2024

Maximizing Power Point Tracking (MPPT) is an essential technique in photovoltaic (PV) systems that guarantees the highest potential conversion of sunlight energy under any irradiance changes. Efficient and reliable MPPT a challenge faced by researchers due to factors such as fluctuations presence partial shading. This paper introduced novel hybrid Equilibrium Slime Mould Optimization (ESMO) MPPT-based algorithm combining advantages two recent algorithms, (SMO) Optimizer (EO). The ESMO compared with highly efficient techniques SMO, EO, Particle Swarm (PSO), Grey Wolf (GWO), Whale Algorithm (WOA), both Simulink environment real-time experimental laboratory setup using Dspace1104 controller PV emulator. comparison focuses on performance several cases, including instant change, shading, complex dynamic key advantage fact it has single tunable parameter, which makes implementation much easier and, at same time, reduces computational resources are required control system. Extensive testing proves superiority over all other techniques, average efficiency 99.98% conditions. Additionally, provides fast tracking times 244 ms simulation experiments 200 for experiments. These results show can be very important future large-scale solar systems.

Language: Английский

Citations

1
Two New Bio-inspired Particle Swarm Optimisation Algorithms for Single-Objective Continuous Variable Problems Based on Eavesdropping and Altruistic Animal Behaviours DOI Open Access
Fevzi Tugrul Varna, Phil Husbands

Published: July 3, 2024

This paper presents two novel bio-inspired particle swarm optimisation (PSO) variants: biased eavesdropping PSO (BEPSO) and altruistic heterogeneous (AHPSO). These algorithms are inspired by types of group behaviour found in nature that have not previously been exploited search algorithms. The primary the BEPSO algorithm is observed coupled with a cognitive bias mechanism enables particles to make decisions on cooperation. second algorithm, AHPSO, conceptualises as energy-driven agents which allows formation lending-borrowing relationships. mechanisms underlying these provide new approaches maintaining diversity contributes preventing premature convergence. were tested 30, 50 100-dimensional CEC'13, CEC'14 CEC'17 test suites, various constrained real-world problems, against 13 well-known variants CEC competition winner, differential evolution L-SHADE. experimental results show both algorithms, very competitive performance unconstrained suites problems. They significantly better than most other variant problem sets no comparator was either them any 100-d sets.

Language: Английский

Citations

1