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11
A Review of Multiobjective Test Problems and a Scalable Test Problem Toolkit
- IEEE Transactions on Evolutionary Computation
, 2006
"... Abstract—When attempting to better understand the strengths and weaknesses of an algorithm, it is important to have a strong understanding of the problem at hand. This is true for the field of multiobjective evolutionary algorithms (EAs) as it is for any other field. Many of the multiobjective test ..."
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Abstract—When attempting to better understand the strengths and weaknesses of an algorithm, it is important to have a strong understanding of the problem at hand. This is true for the field of multiobjective evolutionary algorithms (EAs) as it is for any other field. Many of the multiobjective test problems employed in the EA literature have not been rigorously analyzed, which makes it difficult to draw accurate conclusions about the strengths and weaknesses of the algorithms tested on them. In this paper, we systematically review and analyze many problems from the EA literature, each belonging to the important class of real-valued, unconstrained, multiobjective test problems. To support this, we first introduce a set of test problem criteria, which are in turn supported by a set of definitions. Our analysis of test problems highlights a number of areas re-quiring attention. Not only are many test problems poorly con-structed but also the important class of nonseparable problems, particularly nonseparable multimodal problems, is poorly repre-sented. Motivated by these findings, we present a flexible toolkit for constructing well-designed test problems. We also present em-pirical results demonstrating how the toolkit can be used to test an optimizer in ways that existing test suites do not. Index Terms—Evolutionary algorithms (EAs), multiobjective evolutionary algorithms, multiobjective optimization, multiobjec-tive test problems. I.
An Evolution Strategy with Probabilistic Mutation for Multi-Objective Optimisation
- IN CEC ’03
, 2003
"... Evolutionary algorithms have been applied with great success to the difficult field of multi-objective optimisation. Nevertheless, the need for improvements in this field is still strong. We present a new evolutionary algorithm, ESP (the Evolution Strategy with Probabilistic mutation). ESP extends t ..."
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Cited by 14 (4 self)
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Evolutionary algorithms have been applied with great success to the difficult field of multi-objective optimisation. Nevertheless, the need for improvements in this field is still strong. We present a new evolutionary algorithm, ESP (the Evolution Strategy with Probabilistic mutation). ESP extends traditional evolution strategies in two principal ways: it applies mutation probabilistically in a GA-like fashion, and it uses a new hypervolume based, parameterless, scaling independent measure for resolving ties during the selection process. ESP outperforms the state-of-the-art algorithms on a suite of benchmark multi-objective test functions using a range of popular metrics.
Evolutionary computing in manufacturing industry: an overview of recent applications
, 2005
"... Traditional methods often employed to solve complex real world problems tend to inhibit elaborate exploration of the search space. They can be expensive and often results in sub-optimal solutions. Evolutionary computation (EC) is generating considerable interest for solving real world engineering pr ..."
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Cited by 9 (1 self)
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Traditional methods often employed to solve complex real world problems tend to inhibit elaborate exploration of the search space. They can be expensive and often results in sub-optimal solutions. Evolutionary computation (EC) is generating considerable interest for solving real world engineering problems. They are proving robust in delivering global optimal solutions and helping to resolve limitations encountered in traditional methods. EC harnesses the power of natural selection to turn computers into optimisation tools. The core methodologies of EC are genetic algorithms (GA), evolutionary programming (EP), evolution strategies (ES) and genetic programming (GP). This paper attempts to bridge the gap between theory and practice by exploring characteristics of real world problems and by surveying recent EC applications for solving real world problems in the manufacturing industry. The survey outlines the current status and trends of EC applications in manufacturing industry. For each application domain, the paper describes the general domain problem, common issues, current trends, and the improvements generated by adopting the GA strategy. The paper concludes with an outline of inhibitors to industrial applications of optimisation algorithms.
Applying Evolutionary Algorithms to Problems with Noisy, Time-consuming Fitness Functions
, 2004
"... For many "real world" applications of evolutionary computation, the fitness function is obscured by random noise. This interferes with the evaluation and selection process and adversely affects the performance of the algorithm. We present a study of noise compensation techniques designed t ..."
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Cited by 7 (0 self)
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For many "real world" applications of evolutionary computation, the fitness function is obscured by random noise. This interferes with the evaluation and selection process and adversely affects the performance of the algorithm. We present a study of noise compensation techniques designed to better counteract the negative effects of noise. We introduce algorithms that vary the number of samples used per candidate based on the amount of noise present at that point in the search space. Results show that these algorithms are significantly better than the traditional technique used by the optimisation community and that noise compensation is indeed a difficult task that warrants further investigation.
Map-labelling with a Multi-objective Evolutionary Algorithm
"... We present a multi-objective evolutionary algorithm approach to the map-labelling problem. Map-labelling involves placing labels for sites onto a map such that the result is easy to read and usable for navigation. However, map-users vary in their priorities and capabilities: for example, sightimpair ..."
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We present a multi-objective evolutionary algorithm approach to the map-labelling problem. Map-labelling involves placing labels for sites onto a map such that the result is easy to read and usable for navigation. However, map-users vary in their priorities and capabilities: for example, sightimpaired users need to maximise font-size, whereas other users may be willing to accept smaller labels in exchange for increased clarity of bindings of labels to sites. With a multiobjective approach, we evolve a range of labellings from which users can select according to their particular circumstances. We present results from labelling two maps, including a difficult, dense map of Newcastle County in Delaware, which clearly illustrate the advantages of the multi-objective approach.
Designing Comminution Circuits with a Multi-Objective Evolutionary Algorithm
"... Abstract- Mining is an important industry in Australia, contributing billions of dollars to the economy. The performance of a processing plant has a large impact on the profitability of a mining operation, yet plant design decisions are typically guided more by intuition and experience than by analy ..."
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Abstract- Mining is an important industry in Australia, contributing billions of dollars to the economy. The performance of a processing plant has a large impact on the profitability of a mining operation, yet plant design decisions are typically guided more by intuition and experience than by analysis. In this paper, we motivate the use of an evolutionary algorithm to aid in the design of such plants. We formalise plant design in terms suitable for application in a multi-objective evolutionary algorithm and create a simulation to assess the performance of candidate solutions. Results show the effectiveness of this approach with our algorithm producing designs superior to those used in practice today, promising significant financial benefits. 1
A Review of Multiobjective Test Problems and a Scalable Test Problem Toolkit
"... Abstract—When attempting to better understand the strengths and weaknesses of an algorithm, it is important to have a strong understanding of the problem at hand. This is true for the field of multiobjective evolutionary algorithms (EAs) as it is for any other field. Many of the multiobjective test ..."
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Abstract—When attempting to better understand the strengths and weaknesses of an algorithm, it is important to have a strong understanding of the problem at hand. This is true for the field of multiobjective evolutionary algorithms (EAs) as it is for any other field. Many of the multiobjective test problems employed in the EA literature have not been rigorously analyzed, which makes it difficult to draw accurate conclusions about the strengths and weaknesses of the algorithms tested on them. In this paper, we systematically review and analyze many problems from the EA literature, each belonging to the important class of real-valued, unconstrained, multiobjective test problems. To support this, we first introduce a set of test problem criteria, which are in turn supported by a set of definitions. Our analysis of test problems highlights a number of areas requiring attention. Not only are many test problems poorly constructed but also the important class of nonseparable problems, particularly nonseparable multimodal problems, is poorly represented. Motivated by these findings, we present a flexible toolkit for constructing well-designed test problems. We also present empirical results demonstrating how the toolkit can be used to test an optimizer in ways that existing test suites do not. Index Terms—Evolutionary algorithms (EAs), multiobjective evolutionary algorithms, multiobjective optimization, multiobjective test problems. I.
Automated Evolutionary Design of Self-Assembly and Self-Organising Systems
"... Self-assembly and self-organisation are natural construction processes where the spontaneous formation of aggregates emerges throughout the progressive interplay of local interactions among its constituents. Made upon cooperative self-reliant components, selfassembly and self-organising systems are ..."
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Self-assembly and self-organisation are natural construction processes where the spontaneous formation of aggregates emerges throughout the progressive interplay of local interactions among its constituents. Made upon cooperative self-reliant components, selfassembly and self-organising systems are seen as distributed, not necessarily synchronous, autopoietic mechanisms for the bottom-up fabrication of supra-structures. The systematic understanding of how nature endows these autonomous components with sufficient “intelligence” to combine themselves to form useful aggregates brings challenging questions to science, answers to which have many potential applications in matters of life and technological advances. It is for this reason that the investigation to be presented along this thesis focuses on the automated design of self-assembly and self-organizing systems by means of artificial evolution. Towards this goal, this dissertation embodies research on evolutionary algorithms applied to the parameters design of a computational model of self-organisation and the components design of a computational model of self-assembly. In addition, an analytical assessment combining correlation metrics and clustering, as well as the exploration of emergent patterns of cooperativity and the measurement of activity across evolution, is made. The results support the research hypothesis that an adaptive process such as artificial evolution is indeed a suitable strategy for the automated design of self-assembly and self-organising systems where local interactions, homogeneity and both stochastic and discrete models of execution play a crucial role in emergent complex structures. ii Acknowledgements I would like to thank my academic supervisors Dr. Natalio Krasnogor and Prof. Graham Kendall for all the support, guidance, discussions, ideas and advice during my Ph.D. career. I also thank Prof. Edmund Burke for providing the opportunity to study in the ASAP Group. I am specially grateful to my financial sponsors Universities UK for the Overseas
A Multi-Objective Evolutionary Algorithm Approach
- Minerals Engineering
, 2004
"... The performance of crushing equipment in mineral processing circuits is often critical to the generation of final product. A multi-objective evolutionary algorithm has been developed that allows the crusher internal geometry to be created and evaluated against multiple performance objectives. The ..."
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The performance of crushing equipment in mineral processing circuits is often critical to the generation of final product. A multi-objective evolutionary algorithm has been developed that allows the crusher internal geometry to be created and evaluated against multiple performance objectives. The multiple-objective approach is particularly important in mineral processing, as the optimum performance of single machines is often a trade-off between competing process drivers. A case study is presented that demonstrates the application of the technique to the design of cone crusher liners. New crusher liner profiles resulting from the application of the evolutionary algorithm suggest that significant improvements in the generation of lump product can be obtained. The extension of the approach to wider process plant design is discussed in terms of the objectives and issues to be addressed.
Tecnológico de Monterrey,
"... Fan-shape optimisation using CFD and genetic algorithms for increasing the efficiency of electric motors ..."
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Fan-shape optimisation using CFD and genetic algorithms for increasing the efficiency of electric motors
