A comparative study of local search within a surrogate-assisted multi-objective memetic algorithm framework for expensive problems

© 2016 Elsevier B.V. All rights reserved. A comparative study of the impacts of various local search methodologies for the surrogate-assisted multi-objective memetic algorithm (MOMA) is presented in this paper. The base algorithm for the comparative study is the single surrogate-assisted MOMA (SS-MOMA) with the main aim being to solve expensive problems with a limited computational budget. In addition to the standard weighted sum (WS) method used in the original SS-MOMA, we studied the capabilities of other local search methods based on the achievement scalarizing function (ASF), Chebyshev function, and random mutation hill climber (RMHC) in various test problems. Several practical aspects, such as normalization and constraint handling, were also studied and implemented to deal with real-world problems. Results from the test problems showed that, in general, the SS-MOMA with ASF and Chebyshev functions was able to find higher-quality solutions that were more robust than those found with WS or RMHC; although on problems with more complicated Pareto sets SS-MOMA-WS appeared as the best. SS-MOMA-ASF in conjunction with the Chebyshev function was then tested on an airfoil-optimization problem and compared with SS-MOMA-WS and the non-dominated sorting based genetic algorithm-II (NSGA-II). The results from the airfoil problem clearly showed that SS-MOMA with an achievement-type function could find more diverse solutions than SS-MOMA-WS and NSGA-II. This suggested that for real-world applications, higher-quality solutions are more likely to be found when the surrogate-based memetic optimizer is equipped with ASF or a Chebyshev function than with other local search methods

A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms

This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this record.Evolutionary algorithms are widely used for solving multiobjective optimization problems but are often criticized because of a large number of function evaluations needed. Approximations, especially function approximations, also referred to as surrogates or metamodels are commonly used in the literature to reduce the computation time. This paper presents a survey of 45 different recent algorithms proposed in the literature between 2008 and 2016 to handle computationally expensive multiobjective optimization problems. Several algorithms are discussed based on what kind of an approximation such as problem, function or fitness approximation they use. Most emphasis is given to function approximation-based algorithms. We also compare these algorithms based on different criteria such as metamodeling technique and evolutionary algorithm used, type and dimensions of the problem solved, handling constraints, training time and the type of evolution control. Furthermore, we identify and discuss some promising elements and major issues among algorithms in the literature related to using an approximation and numerical settings used. In addition, we discuss selecting an algorithm to solve a given computationally expensive multiobjective optimization problem based on the dimensions in both objective and decision spaces and the computation budget available.The research of Tinkle Chugh was funded by the COMAS Doctoral Program (at the University of Jyväskylä) and FiDiPro Project DeCoMo (funded by Tekes, the Finnish Funding Agency for Innovation), and the research of Dr. Karthik Sindhya was funded by SIMPRO project funded by Tekes as well as DeCoMo

Benefit analysis of using soft DC links in medium voltage distribution networks

Soft DC Links are power electronic converters enabling the control of power flow between distribution feeders or networks. This thesis considers the use of Soft DC Links in Medium Voltage (MV) distribution networks to improve network operation while facilitating the integration of distributed generators (DGs). Soft DC Links include Soft Open Points (SOPs) and Medium Voltage Direct Current (MVDC) links. An SOP can be installed to replace mechanical switchgear in a network, providing controllable active power exchange between connected feeders, as well as reactive power compensation at each interface terminal. The deployment of an MVDC link enables power and voltage controls over a wider area, and facilitates the effective use of available capacity between adjacent networks. The benefits of using SOP and MVDC link in MV distribution networks were investigated. A multi-objective optimisation framework was proposed to quantify the operational benefits of a distribution network with an SOP. An optimisation method integrating both global and local search techniques was developed to determine the set-points of an SOP. It was found that an SOP can improve network operation along multiple criteria and facilitate the integration capacity of DGs. A Grid Transformer-based control method of an MVDC link was proposed, which requires only measurements at the grid transformers to determine the operation of an MVDC link. Control strategies considering different objectives were developed. The proposed control method is used in the ANGLE-DC project, which aims to trial the first MVDC link in Europe by converting an existing AC circuit to DC operation. It was found that an MVDC link can significantly increase the network hosting capacity for DG connections while reducing network losses compared to an AC line. An impact quantification of Soft DC Links was carried out on statistically-similar distribution networks, which refer to a set of networks with similar but different topological and electrical properties. A model was developed to determine the optimal allocation of Soft DC Links. It was found that a Soft DC Link can reduce the network annual cost under a wide range of DG penetration conditions. The statistical analysis provides distribution network planners with more robust decisions on the implementation of Soft DC Links