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About 10,000 papers for “multi-objective evolutionary algorithms”

Multi-Objective Evolutionary Algorithms

A. K. Nandi, Kalyanmoy Deb · IGI Global eBooks · 2017

The primary objective in designing appropriate particle reinforced polyurethane composite which will be used as a mould material in soft tooling process is to minimize the cycle time of soft tooling process by providing faster cooling rate during solidification of wax/plastic component. This chapter exemplifies an effective approach to design particle reinforced mould materials by solving the inherent multi-objective optimization problem associated with soft tooling process using evolutionary algorithms. In this chapter, first a brief introduction of multi-objective optimization problem with the key issues is presented. Then, after a short overview on the working procedure of genetic algorithm, a well- established multi-objective evolutionary algorithm, namely NSGA-II along with various performance metrics are described. The inherent multi-objective problem in soft tooling process is demonstrated and subsequently solved using an elitist non-dominated sorting genetic algorithm, NSGA-II. Multi-objective optimization results obtained using NSGA-II are analyzed statistically and validated with real industrial application. Finally the fundamental results of this approach are summarized and various perspectives to the industries along with scopes for future research work are pointed out.

Cited by 1

Evolutionary Multi-Objective Algorithms

Aurora Torres Soto, Dolores Torres, Sergio Enríquez et al. · InTech eBooks · 2012

The versatility that genetic algorithm (GA) has proved to have for solving different problems, has make it the first choice of researchers to deal with new challenges. Currently, GAs are the most well known evolutionary algorithms, because their intuitive principle of operation and their relatively simple implementation; besides they have the ability to reflect the philosophy of evolutionary computation in an easy and quick way.

Cited by 3Open access

Indicator-based Multi-objective Evolutionary Algorithms

Jesús Guillermo Falcón-Cardona, Carlos A. Coello Coello · ACM Computing Surveys · 2020

For over 25 years, most multi-objective evolutionary algorithms (MOEAs) have adopted selection criteria based on Pareto dominance. However, the performance of Pareto-based MOEAs quickly degrades when solving multi-objective optimization problems (MOPs) having four or more objective functions (the so-called many-objective optimization problems), mainly because of the loss of selection pressure. Consequently, in recent years, MOEAs have been coupled with indicator-based selection mechanisms in furtherance of increasing the selection pressure so that they can properly solve many-objective optimization problems. Several research efforts have been conducted since 2003 regarding the design of the so-called indicator-based (IB) MOEAs. In this article, we present a comprehensive survey of IB-MOEAs for continuous search spaces since their origins up to the current state-of-the-art approaches. We propose a taxonomy that classifies IB-mechanisms into two main categories: (1) IB-Selection (which is divided into IB-Environmental Selection, IB-Density Estimation, and IB-Archiving) and (2) IB-Mating Selection. Each of these classes is discussed in detail in this article, emphasizing the advantages and drawbacks of the selection mechanisms. In the final part, we provide some possible paths for future research.

Cited by 170

Multi-Objective Evolutionary Algorithms for the

Risk-Return Trade-Off In, Amitabha Mukerjee, Rita Biswas et al. · 2007

Multi-Criteria Decision Making is an increasingly accepted tool for decision making in management. In this work, we highlight the application of a novel Multi-Objective Evolutionary Algorithm, NSGA-II to the risk-return trade-off for a bank loan portfolio manager.

Cited by 0

Multi-objective optimization using evolutionary algorithms

David Griffel · The Mathematical Gazette · 2003

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.

Cited by 11Open access

Applications of Multi-Objective Evolutionary Algorithms

Carlos A. Coello Coello, Gary B. Lamont · Advances in natural computation · 2004

An Introduction to Multi-Objective Evolutionary Algorithms and Their Applications Optimal Design of Industrial Electromagnetic Devices: A Multiobjective Evolutionary Approach Using a Particle Swarm Optimizer with a Multi-Objective Selection Scheme to Design Combinational Logic Circuits Automatic Control System Design via a Multiobjective Evolutionary Algorithm Evolutionary Multi-Objective Optimization of Trusses A Multi-Objective Evolutionary Algorithm for the Covering Tour Problem Multiobjective Aerodynamic Design and Visualization of Supersonic Wings by Using Adaptive Range Multiobjective Genetic Algorithms Mutli-Objective Spectroscopic Data Analysis of Inertial Confinement Fusion Implosion Cores: Plasma Gradient Determination On Machine Learning with Multiobjective Genetic Optimization and other papers.

Cited by 529

Multi-objective Evolutionary Algorithms for Single-objective Constrained Optimisation

Tao · 2026

In the real world there are a large number of optimization problems, especially in scientific research and engineering practice, which often have constraints and sometimes more than one objective. Due to the different characteristics of the problems themselves, traditional methods in operations research are no longer able to solve them independently. Evolutionary algorithms (EAs), as a global optimization method based on group search, are well suited for solving constrained optimization problems and multi-objective optimization problems. Therefore, evolutionary optimization has received increasing attention from researchers. The aim of this thesis is to design efficient multi-objective evolutionary algorithms (MOEAs) and strategies for constrained single-objective optimization problems (CSOPs) through in-depth exploration, and to conduct corresponding theoretical and experimental analysis. Specifically, the main research work of this thesis includes the following aspects. Firstly, we consider a many-objective method for solving CSOPs. The method keeps the standard objectives: the original objective function and the sum of the degrees of constraint violation. Besides them, more objectives are added into the method. One objective is based on the feasible rule. The others come from the penalty function method. Then a multiobjective differential evolution algorithm is applied to solving multi-objective optimization problems with two, three and four objectives. An experimental study on thirteen benchmark functions from IEEE CEC2006 Competition is conducted. Experimental results confirm our expectation that adding more objectives could be useful and the solution quality is improved. Secondly, we construct a new multi-objective evolutionary framework for solving CSOPs, which works by converting a CSOP into a problem with helper and equivalent objectives (HECO). An equivalent objective means that its optimal solution set is the same as that to the constrained problem but a helper objective does not. Then this multi-objective optimization problem is decomposed into a group of sub-problems using the weighted sum approach. Weights are dynamically adjusted so that each subproblem eventually tends to a problem with an equivalent objective. We theoretically analyze the computation time of the helper and equivalent objective method on a hard problem called “wide gap”. In a “wide gap” problem, an algorithm needs exponential time to cross between two fitness levels (a wide gap). We prove that using helper and equivalent objectives can shorten the time of crossing the “wide gap”. A series of derivative algorithms are then designed based on HECO, such as HECO-DE, HECO-DEtch and HECO-DEm. Extensive experimental studies show these algorithms perform much better in solving benchmark problems in IEEE CEC2017/2018 Competition, and IEEE CEC2006 Competition than other state-of-the-art EAs. Finally, apart from constraint handling techniques, we also contribute to new search operators which lead to further improvement our EAs for solving CSOPs. Two methods of studying valleys on a fitness landscape. Afterwards, the principle component analysis (PCA) could be used to characterize fitness landscapes. Based on this finding, a new search operator, called PCA-projection, is proposed. In order to verify the effectiveness of PCA-projection, we design two algorithms enhanced with PCA-projection for solving CSOPs. Experiment results indicate that the new search operator based on PCA-projection works very well

Cited by 0Open access

Multi‐Objective Evolutionary Algorithms and Evolutionary Large‐Scale Optimization

Xingyi Zhang, Ran Cheng, Ye Tian et al. · 2024

This chapter focuses on problems of large scale multi-objective optimization problems (LSMOPs) and large scale many-objective optimization problems. It refers to research adopting evolutionary algorithms (EAs) to solve large-scale optimization problems as evolutionary large-scale optimization. The chapter introduces multi-objective EAs and overviews evolutionary large-scale optimization. On the one hand, to tailor EAs for solving LSMOPs, the related works are highly related to multi-objective evolutionary algorithms (MOEAs). While conventional MOEAs were developed to solve problems with relatively small-scale decision variables, the core mechanisms behind them have founded further development toward evolutionary large-scale multi-objective optimization. On the other hand, when dealing with large-scale decision variables, ideas are also borrowed from the single objective optimization literature. Particularly, the divide-and-conquer strategy deals with large-scale decision variables.

Cited by 0

Performance Scaling of Multi-Objective Evolutionary Algorithms

Vaibhav Khare, Prof Xin Yao, Kalyanmoy Deb · 2007

In real world problems, one is often faced with the problem of multiple, possibly competing, goals, which should be optimized simultaneously. These competing goals give rise to a set of compromise solutions, generally denoted as Pareto-optimal. If none of the objectives have preference over the other, none of these trade-off solutions can be said to be better than any other solution in the set. Multi-objective Evolutionary Algorithms (MOEAs) can find these optimal trade-offs in order to get a set of solutions that are optimal in an overall sense.

Cited by 0

Dynamic multi-objective optimization using evolutionary algorithms

Herring, Daniel · University of Birmingham Institutional Research Archive (University of Birmingham) · 2023

Dynamic Multi-objective Optimization Problems (DMOPs) offer an opportunity to examine and solve challenging real world scenarios where trade-off solutions between conflicting objectives change over time. Definition of benchmark problems allows modelling of industry scenarios across transport, power and communications networks, manufacturing and logistics. Recently, significant progress has been made in the variety and complexity of DMOP benchmarks and the incorporation of realistic dynamic characteristics. However, significant gaps still exist in standardised methodology for DMOPs, specific problem domain examples and in the understanding of the impacts and explanations of dynamic characteristics. This thesis provides major contributions on these three topics within evolutionary dynamic multi-objective optimization. Firstly, experimental protocols for DMOPs are varied. This limits the applicability and relevance of results produced and conclusions made in the field. A major source of the inconsistency lies in the parameters used to define specific problem instances being examined. The uninformed selection of these has historically held back understanding of their impacts and standardisation in experimental approach to these parameters in the multi-objective problem domain. Using the frequency and severity (or magnitude) of change events, a more informed approach to DMOP experimentation is conceptualized, implemented and evaluated. Establishment of a baseline performance expectation across a comprehensive range of dynamic instances for well-studied DMOP benchmarks is analyzed. To maximize relevance, these profiles are composed from the performance of evolutionary algorithms commonly used for baseline comparisons and those with simple dynamic responses. Comparison and contrast with the coverage of parameter combinations in the sampled literature highlights the importance of these contributions. Secondly, the provision of useful and realistic DMOPs in the combinatorial domain is limited in previous literature. A novel dynamic benchmark problem is presented by the extension of the Travelling Thief Problem (TTP) to include a variety of realistic and contextually justified dynamic changes. Investigation of problem information exploitation and it's potential application as a dynamic response is a key output of these results and context is provided through comparison to results obtained by adapting existing TTP heuristics. Observation driven iterative development prompted the investigation of multi-population island model strategies, together with improvements in the approaches to accurately describe and compare the performance of algorithm models for DMOPs, a contribution which is applicable beyond the dynamic TTP. Thirdly, the purpose of DMOPs is to reconstruct realistic scenarios, or features from them, to allow for experimentation and development of better optimization algorithms. However, numerous important characteristics from real systems still require implementation and will drive research and development of algorithms and mechanisms to handle these industrially relevant problem classes. The novel challenges associated with these implementations are significant and diverse, even for a simple development such as consideration of DMOPs with multiple time dependencies. Real world systems with dynamics are likely to contain multiple temporally changing aspects, particularly in energy and transport domains. Problems with more than one dynamic problem component allow for asynchronous changes and a differing severity between components that leads to an explosion in the size of the possible dynamic instance space. Both continuous and combinatorial problem domains require structured investigation into the best practices for experimental design, algorithm application and performance measurement, comparison and visualization. Highlighting the challenges, the key requirements for effective progress and recommendations on experimentation are explored here.

Cited by 0