Chapter 2 Optimisation Algorithms

Abstract Optimisation problems are ubiquitous in particle and astrophysics, and involve locating the optimum of a complicated function of many parameters that may be computationally expensive to evaluate. We describe a number of global optimisation algorithms that are not yet widely used in particle astrophysics, benchmark them against random sampling and existing techniques, and perform a detailed comparison of their performance on a range of test functions. These include four analytic test functions of varying dimensionality, and a realistic example derived from a recent global fit of weak-scale supersymmetry. Although the best algorithm to use depends on the function being investigated, we are able to present general conclusions about the relative merits of random sampling, Differential Evolution, Particle Swarm Optimisation, the Covariance Matrix Adaptation Evolution Strategy, Bayesian Optimisation, Grey Wolf Optimisation, and the PyGMO Artificial Bee Colony, Gaussian Particle Filter and Adaptive Memory Programming for Global Optimisation algorithms.

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Multi-Mode Wave Energy Converter Design Optimisation Using an Improved Moth Flame Optimisation Algorithm

Energies ◽

10.3390/en14133737 ◽

2021 ◽

Vol 14 (13) ◽

pp. 3737

Author(s):

Mehdi Neshat ◽

Nataliia Sergiienko ◽

Seyedali Mirjalili ◽

Meysam Majidi Nezhad ◽

Giuseppe Piras ◽

...

Keyword(s):

Wave Energy ◽

Wave Energy Converter ◽

Total Power ◽

Energy Converter ◽

Wave Regime ◽

Optimisation Algorithm ◽

Wide Range ◽

Evolutionary Optimisation ◽

Optimisation Methods ◽

Optimisation Algorithms

Ocean renewable wave power is one of the more encouraging inexhaustible energy sources, with the potential to be exploited for nearly 337 GW worldwide. However, compared with other sources of renewables, wave energy technologies have not been fully developed, and the produced energy price is not as competitive as that of wind or solar renewable technologies. In order to commercialise ocean wave technologies, a wide range of optimisation methodologies have been proposed in the last decade. However, evaluations and comparisons of the performance of state-of-the-art bio-inspired optimisation algorithms have not been contemplated for wave energy converters’ optimisation. In this work, we conduct a comprehensive investigation, evaluation and comparison of the optimisation of the geometry, tether angles and power take-off (PTO) settings of a wave energy converter (WEC) using bio-inspired swarm-evolutionary optimisation algorithms based on a sample wave regime at a site in the Mediterranean Sea, in the west of Sicily, Italy. An improved version of a recent optimisation algorithm, called the Moth–Flame Optimiser (MFO), is also proposed for this application area. The results demonstrated that the proposed MFO can outperform other optimisation methods in maximising the total power harnessed from a WEC.

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Implementation of dimensional management methodology and optimisation algorithms for pre-drilled hinge line interfaces of critical aero assemblies – A case study

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture ◽

10.1177/0954405420978101 ◽

2020 ◽

pp. 095440542097810

Author(s):

Konstantinos C Bacharoudis ◽

David Bainbridge ◽

Alison Turner ◽

Atanas A Popov ◽

Svetan M Ratchev

Keyword(s):

Degrees Of Freedom ◽

Engineering Problem ◽

Hole Diameter ◽

Design Stage ◽

Assembly Process ◽

Mechanical Assembly ◽

Hinge Line ◽

Management Procedure ◽

Dimensional Management ◽

Optimisation Algorithms

A dimensional management procedure is developed and implemented in this work to deal with the identification of the optimum hole diameter that needs to be pre-drilled in order to successfully join two subassemblies in a common hinge line interface when most of the degrees of freedom of each subassembly have already been constrained. Therefore, an appropriate measure is suggested that considers the assembly process and permits the application of optimisation algorithms for the identification of the optimum hole diameter. The complexity of the mechanical subassemblies requires advanced 3D tolerance analysis techniques to be implemented and the matrix method was adopted. The methodology was demonstrated for an industrial, aerospace engineering problem, that is, the assembly of the joined wing configuration of the RACER compound rotorcraft of AIRBUS Helicopter and the necessary tooling needed to build the assembly. The results indicated that hinge line interfaces can be pre-opened at a sufficiently large size and thus, accelerate the assembly process whilst the suggested methodology can be used as a decision-making tool at the design stage of this type of mechanical assembly.

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Parameter settings in particle swarm optimisation algorithms: a survey

International Journal of Automation and Control ◽

10.1504/ijaac.2022.10044251 ◽

2022 ◽

Vol 16 (2) ◽

pp. 164

Author(s):

Shi Cheng ◽

Jing Li

Keyword(s):

Particle Swarm ◽

Particle Swarm Optimisation ◽

Optimisation Algorithms

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Performance Comparison of Recent Population-Based Metaheuristic Optimisation Algorithms in Mechanical Design Problems of Machinery Components

Machines ◽

10.3390/machines9120341 ◽

2021 ◽

Vol 9 (12) ◽

pp. 341

Author(s):

Bugra Alkan ◽

Malarvizhi Kaniappan Chinnathai

Keyword(s):

Engineering Design ◽

Mechanical Design ◽

Search Algorithm ◽

Computation Time ◽

Population Based ◽

Performance Comparison ◽

Design Problems ◽

Component Design ◽

Engineering Design Problems ◽

Optimisation Algorithms

The optimisation of complex engineering design problems is highly challenging due to the consideration of various design variables. To obtain acceptable near-optimal solutions within reasonable computation time, metaheuristics can be employed for such problems. However, a plethora of novel metaheuristic algorithms are developed and constantly improved and hence it is important to evaluate the applicability of the novel optimisation strategies and compare their performance using real-world engineering design problems. Therefore, in this paper, eight recent population-based metaheuristic optimisation algorithms—African Vultures Optimisation Algorithm (AVOA), Crystal Structure Algorithm (CryStAl), Human-Behaviour Based Optimisation (HBBO), Gradient-Based Optimiser (GBO), Gorilla Troops Optimiser (GTO), Runge–Kutta optimiser (RUN), Social Network Search (SNS) and Sparrow Search Algorithm (SSA)—are applied to five different mechanical component design problems and their performance on such problems are compared. The results show that the SNS algorithm is consistent, robust and provides better quality solutions at a relatively fast computation time for the considered design problems. GTO and GBO also show comparable performance across the considered problems and AVOA is the most efficient in terms of computation time.

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