Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 16 of 16 for “"Multi-objective evolutionary algorithms"”.
-
Multi-objective evolutionary algorithms for data clustering
In this work we investigate the use of Multi-Objective metaheuristics for the data-mining task of clustering. We �first investigate methods of evaluating the quality of clustering solutions, we then propose a new Multi-Objective clustering algorithm driven by multiple measures of cluster quality …
-
Multi-objective evolutionary algorithms for product design
… Chemistry and Machine Learning (ML) with Evolutionary MultiObjective Optimisation (EMOO) techniques to streamline compound design. This approach automates the design process by leveraging ML to accurately predict compound properties and using EMOO to select compounds that meet various …
-
Multi-Objective evolutionary algorithms for vehicle routing problems
The Vehicle Routing Problem, which main objective is to find the lowest-cost set of routes to deliver goods to customers, has many applications in transportation services. In the past, costs have been mainly associated to the number of routes and the travel distance, however, in real-world problems …
-
Design problem optimization with multi-objective evolutionary algorithms
Complex design challenges involve conflicting objectives and require robust optimization techniques. They commonly arise in engineering, building design, robotics, drug design, and energy systems, among others, where balancing competing criteria is essential. Sunshade optimization is also a complex …
-
Optimisation of electrical machines using data-driven dynamic thermal models and surrogate-based multi-objective evolutionary algorithms.
… hand, electrical machine design is inherently a Multi-Objective Optimisation Problem (MOOP). Although Multi-Objective Evolutionary Algorithms (MOEAs) are widely used, they require the evaluation of thousands of candidate designs, making them impractical when fitness function evaluations are …
-
AI Enabled Drug Design and Side Effect Prediction Powered by Multi-Objective Evolutionary Algorithms & Transformer Models
Due to the large search space and conflicting objectives, drug design and discovery is a difficult problem for which new machine learning (ML) approaches are required. Here, the problem is to invent a method by which new, therapeutically useful, compounds can be discovered; and to simultaneously …
-
The stored non-domination level multi-objective evolutionary algorithm
… in this dissertation is in the field of Multi-Objective Evolutionary Algorithms. Evolutionary Algorithms (EAs) are a class of algorithms which model Darwin's theory of evolution to search for solutions to difficult problems. Multi-Objective Evolutionary Algorithms (MOEAs) are a broader …
-
Multi-objective evolutionary neural architecture search for recurrent neural networks
… by the model. This study investigates the use of multi-objective evolutionary algorithms as an exploration strategy for NAS to evolve recurrent neural network (RNN) architectures. This allows for the consideration of the underlying computational resource requirements of the RNN models while …
-
Sustainable Closed-Loop Supply Chaing Network Design
… (MILP), and nondeterministically via Fuzzy Multi-objective Mixed Integer Linear Programming (FMOMILP) model, by considering sustainability and uncertainty. Fuzzy programming approaches were utilized to solve the problem. Two multi-objective evolutionary algorithms were employed to find the …
-
Multi-objective optimisation with financial applications
Portfolio Optimisation is a multi-objective problem which involves finding the allocation of shares in a portfolio that optimises the likely return for a level of risk which an investor is prepared to tolerate. There have been several multi-objective evolutionary algorithms that have been used to …
-
Accelerating MOEA Non-dominated Sorting by Preserving Archival Relationships
… sorting is an important part of many multi-objective evolutionary algorithms (MOEAs). It is used to determine which individuals to keep in the archive of best individuals between generations and to evaluate fitness for breeding. Because this sorting is performed after every generation, …
-
Multi-objective evolutionary methods for time-changing portfolio optimization problems
… of efficient asset allocations with the use of evolutionary algorithms. The portfolio optimization problem is a multi-objective optimization problem for the conflicting criteria of risk and expected return. Furthermore the nonstationary nature of the market makes it a time-changing problem in …
-
Parallelization of hybrid multi-objective evolutionary algorithm on multi-core architectures
Many real world optimization problems involve multiple conflicting objectives, constraints and parameters. Multi-objective optimization (MOO) techniques are used to solve these problems. The goal of MOO is to find a set of optimal solutions, or the Pareto optimal front. Multi-objective evolutionary …
-
Development of an intelligent tool for energy efficient and low environment impact shipping
… In addition to the 3DDP method developed, three multi-objective evolutionary algorithms (MOEAs) are also proposed to treat weather routing as a multi-objective and constrained optimisation problem. Based on ship hydrodynamic knowledge and optimisation algorithms an Intelligent Tool for Energy …
-
Multi-objective Optimization in Traffic Signal Control
… the simulation is very time-consuming. Multi-objective Evolutionary Algorithms (MOEAs) are in many ways superior to traditional search methods. They have been widely utilized in traffic signal optimization problems. However, running MOEAs on traffic optimization problems using …
-
Evolutionary approaches for feature selection in biological data
… 1) to investigate and develop feature selection algorithms that incorporate various evolutionary strategies, 2) using the developed algorithms to find the “most relevant” biomarkers contained in biological datasets and 3) and evaluate the goodness of extracted feature subsets for relevance …