Many-Objective Problems: Challenges and Methods

Jáimes, Antonio López; Coello, Carlos A. Coello

doi:10.1007/978-3-662-43505-2_51

Cited by 14 publications

(5 citation statements)

References 62 publications

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“…Multi-objective optimization problems involve optimizing multiple objectives simultaneously [26]. Various goals often interact with each other, and these interactions can be classified as conflicts and harmony [27]. Conflicting relationships mean that as a goal improves, the other worsens, and harmony means that as a goal improves, the other also improves.…”

Section: Definition Of a Multi-objective Problemmentioning

confidence: 99%

Reliability Optimization of the Honeycomb Sandwich Structure Based on A Neural Network Surrogate Model

Wei,

Zhou,

Zhang

et al. 2023

Materials

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Composite radomes are usually located in the nose of aircraft and are important structural components that protect radar antenna. The finite element model of a radome structure is developed and analyzed in this article. Single-objective deterministic and reliability optimization models based on the minimum total mass of the radome structure were established, and the layer thickness of each part of the honeycomb sandwich radome structure was considered a design variable. A multi-objective deterministic and reliability optimization model for a radome structure with a minimum total mass and maximum buckling critical load was established, and a particle swarm optimization algorithm was used to solve the problem. Our optimized results satisfied the constraints and utilization rate of materials, and structural safety was improved.

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Section: Definition Of a Multi-objective Problemmentioning

confidence: 99%

Reliability Optimization of the Honeycomb Sandwich Structure Based on A Neural Network Surrogate Model

Wei,

Zhou,

Zhang

et al. 2023

Materials

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“…Simultaneously optimizing many (more than three) quality criteria is a challenging task, requiring further studies on existing many-objective optimization techniques to cope with this goal. For example, when more objectives are considered, selecting appropriate individuals for the next generation that can help the population toward the Pareto optimal set is very difficult [115]. Despite some recent success in tackling this issue in EC-based algorithms, we cannot simply use these algorithms because proper modifications are needed to cope better with our problem.…”

Section: Many-objective Optimisationmentioning

confidence: 99%

Comprehensive Quality-Aware Automated Semantic Web Service Composition

Wang

Chen

et al. 2017

AI 2017: Advances in Artificial Intelligence

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Automated web service composition is one of the ultimate goals of serviceoriented computing. It loosely couples web services to accommodate users' complex requirements. Evolutionary Computation (EC) techniques combined with AI planning have been successfully proposed to efficiently produce composite services with near-optimal Quality of Semantic Matchmaking (QoSM) and/or Quality of Service (QoS), which measure the satisfaction of the functional and non-functional requirements from users, respectively. Despite some recent progress, both the effectiveness and efficiency of existing approaches need further improvement to enhance the competitive advantage of service providers. The overall goal of this thesis is to propose novel EC-based fully automated service composition approaches that can effectively and efficiently solve challenging singleobjective, multi-objective, and dynamic service composition problems.Firstly, this thesis proposes two novel Estimation of Distribution Algorithm (EDA) based approaches (called EDA-NHM and EDA-EHM) and one memetic EDA-based approach with four different local search operators to single-objective fully automated web service composition that jointly optimizes QoSM and QoS. EDA-NHM and EDA-EHM are proposed with novel permutation-based and DAG-based representations to model the distribution of composition solutions with respect to varied service composition workflows. Two sampling techniques are also studied in EDA-NHM and EDA-EHM to effectively and efficiently sample new promising permutations and functionally valid DAGs, respectively. These two EDA-based approaches are compared to state-of-the-art works. The comparisons reveal that EDA-NHM produces better-quality composite services than EDA-EHM and the state-of-the-art works. On the other hand, EDA-EHM achieves the highest efficiency among all the competing EC-based methods, delivering moderate effectiveness. Furthermore, one proposed memetic approaches built upon EDA-NHM (called MEEDA-LOP) pushes the cutting-edge performance in terms of effectiveness and efficiency.Secondly, this thesis studies two categories of multi-objective service composition problems: one category aims to generate a set of approximated Pareto optimal solutions for users to choose from, while the other category aims to generate multiple composite services for multiple user segments with distinctive preferences on QoSM. To effectively and efficiently handle the first category of problems, a memetic approach based on Non-dominated Sorting Genetic Algorithm II (NSGA-II), called MNSGA2-EDA, is proposed by enhancing NSGA-II with EDA-based local search. The novelty of this method lies in the innovative use of EDA for effective and efficient local improvements, rather than for global exploration. MNSGA2-EDA is compared to state-of-the-art multi-objective works, for studying its performance. We found that MNSGA2-EDA achieves much higher effectiveness and efficiency in finding Pareto optimal solutions. The second category of problems can be naturally treated as multit...

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“…The so-called dominant resistance solution is a type of special solution that takes extremely poor values on at least one objective, while achieving (approximately) optimal values on the remaining other objectives. Many studies have shown that as the number of objectives increases, the number of DRSs shows an upward trend [31], which exacerbates the adverse impact of ADP on algorithm convergence. The consequence is that the final solution may be widely distributed in the objective space, but far from the desired PF.…”

Section: Introductionmentioning

confidence: 99%

Many-objective Optimization Algorithm Based on the Similarity Principle and Multi-mechanism Collaborative Search

Gan,

Li,

Hao

2024

Preprint

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In order to efficiently solve many-objective optimization problems and maintain a good distribution of solutions, an evolutionary algorithm based on the elimination of similar individuals (ESEA) is proposed in this paper. Considering that environmental selection based on Pareto- dominance relations often yields a small number of dominance-resistant individuals (DRIs), which are difficult to be eliminated naturally during the algorithm iteration process, these DRIs can significantly impact the algorithm's performance. To enhance convergence and stability, ESEA proposes a method to suppress the generation of DRIs by introducing a redundant protection mechanism. Through the elimination of distance-similar individuals in 2D or 3D objective spaces, the ESEA can effectively remove high-density individuals to maintain population diversity and good distribution. By introducing the ISDE+ indicator and eliminating angle-similar individuals in high-dimensional objective spaces, the proposed ESEA can mitigate premature convergence and promote exploration across different regions of the search space, thereby enhancing the likelihood of discovering diverse and potentially superior solutions. The proposed algorithm is compared with five state-of-the-art optimizers on 21 test problems. The experimental results demonstrate the promising performance of the proposed algorithm while effectively searching for the ideal Pareto front. It is evident that eliminating similar individuals and performing multiple mechanism joint searches can enhance the selection pressure towards the ideal Pareto front. Furthermore, the proposed ESEA can not only find a set of well-distributed points on the entire Pareto-optimal front, but also effectively maintain boundary solutions very well.

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Many-Objective Problems: Challenges and Methods

Cited by 14 publications

References 62 publications

Reliability Optimization of the Honeycomb Sandwich Structure Based on A Neural Network Surrogate Model

Reliability Optimization of the Honeycomb Sandwich Structure Based on A Neural Network Surrogate Model

Comprehensive Quality-Aware Automated Semantic Web Service Composition

Many-objective Optimization Algorithm Based on the Similarity Principle and Multi-mechanism Collaborative Search

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