A Framework for Meta-heuristic Parameter Performance Prediction Using Fitness Landscape Analysis and Machine Learning

The behaviour of an optimization algorithm when attempting to solve a problem depends on the values assigned to its control parameters. For an algorithm to obtain desirable performance, its control parameter values must be chosen based on the current problem. Despite being necessary for optimal performance, selecting appropriate control parameter values is time-consuming, computationally expensive, and challenging. As the quantity of control parameters increases, so does the time complexity associated with searching for practical values, which often overshadows addressing the problem at hand, limiting the efficiency of an algorithm. As primarily recognized by the no free lunch theorem, there is no one-size-fits-all to problem-solving; hence from understanding a problem, a tailored approach can substantially help solve it. To predict the performance of control parameter configurations in unseen environments, this thesis crafts an intelligent generalizable framework leveraging machine learning classification and quantitative characteristics about the problem in question. The proposed parameter performance classifier (PPC) framework is extensively explored by training 84 high-accuracy classifiers comprised of multiple sampling methods, fitness types, and binning strategies. Furthermore, the novel framework is utilized in constructing a new parameter-free particle swarm optimization (PSO) variant called PPC-PSO that effectively eliminates the computational cost of parameter tuning, yields competitive performance amongst other leading methodologies across 99 benchmark functions, and is highly accessible to researchers and practitioners. The success of PPC-PSO shows excellent promise for the applicability of the PPC framework in making many more robust parameter-free meta-heuristic algorithms in the future with incredible generalization capabilities

A survey of simulation techniques in commerce and defence

Despite the developments in Modelling and Simulation (M&S) tools and techniques over the past years, there has been a gap in the M&S research and practice in healthcare on developing a toolkit to assist the modellers and simulation practitioners with selecting an appropriate set of techniques. This study is a preliminary step towards this goal. This paper presents some results from a systematic literature survey on applications of M&S in the commerce and defence domains that could inspire some improvements in the healthcare. Interim results show that in the commercial sector Discrete-Event Simulation (DES) has been the most widely used technique with System Dynamics (SD) in second place. However in the defence sector, SD has gained relatively more attention. SD has been found quite useful for qualitative and soft factors analysis. From both the surveys it becomes clear that there is a growing trend towards using hybrid M&S approaches

Multiobjective optimization framework for designing a vehicle suspension system. A comparison of optimization algorithms

[EN] Recent advances in robotics and digital technologies in the automotive industry, allow the integration of vehicle systems with their virtual twins, thus facilitating their modelling and optimization. As a result, the systems design time and manufacturing costs are substantially reduced, while their performance, safety and fatigue life are expanded.This work presents a multiobjective optimization framework for developing an optimal design of a front double wishbone vehicle suspension system based on a four-bar mechanism. This is carried out by coupling several computer-aided design tools (CAD) and computer-aided engineering (CAE) software. The 3D CAD model of the lower control arm of the suspension system is made using SolidWorks (R), the Finite Element Analysis (FEA) of the suspension assembly is modelled using ANSYS (R) Workbench, while the multibody kinetic and dynamic of the designed suspension system is analysed using MSC ADAMS (R). They are embedded in a multidisciplinary optimization design framework (modeFrontier (R)) with the aim of determining the optimal hardpoint locations of a lower control arm by minimizing the chassis pitch accelerations to improve the passengers' comfort, reducing the volume and mass of the suspension system to increase the vehicle stability and manoeuvrability, while decreasing the maximum stresses to extend the system fatigue life and enhancing safety.The methodology has been successfully applied to several driving scenarios entailing different vehicle dy-namics manoeuvres with the aim to find the Pareto optimal front, and to analyse the suspension assembly performance together with the vehicle dynamic behaviour. Results show that the use of such approach may significantly improve the design of the suspension system. Furthermore, a comparison of different optimization strategies and algorithms is performed.Llopis-Albert, C.; Rubio Montoya, FJ.; Zeng, S. (2023). Multiobjective optimization framework for designing a vehicle suspension system. A comparison of optimization algorithms. Advances in Engineering Software. 176(103375). https://doi.org/10.1016/j.advengsoft.2022.10337517610337

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Copyright © 2014 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Environmental Modelling and Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling and Software Vol. 62 (2014), DOI: 10.1016/j.envsoft.2014.09.013The development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g. model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing