☆ 4.7 Article

Weak relationship indicator-based evolutionary algorithm for multimodal multi-objective optimization

INFORMATION SCIENCES (2024)

Related references

Note: Only part of the references are listed.

Article Computer Science, Artificial Intelligence

Hierarchy Ranking Method for Multimodal Multiobjective Optimization With Local Pareto Fronts

Wenhua Li et al.

Summary: This study proposes several benchmark problems with multiple local Pareto fronts and an evolutionary algorithm with a hierarchy ranking method (HREA) to find both the global and local Pareto fronts. Experimental results show that HREA is competitive compared with other state-of-the-art MMEAs for solving the chosen benchmark problems.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2023)

Add to Collection

Article Computer Science, Information Systems

An evolutionary algorithm based on independently evolving sub-problems for multimodal multi-objective optimization

Jialiang Zhang et al.

Summary: This paper proposes a multi-modal multi-objective evolutionary algorithm (MMEAs) based on independently evolving sub-problems to solve the problem of poor diversity maintenance in traditional algorithms. A two-stage environmental selection strategy is used to ensure the convergence of the objective space and the distribution of the decision space. The k-nearest neighbor deletion strategy is employed in the decision space to guarantee the distributivity of each equivalent Pareto optimal solution.

INFORMATION SCIENCES (2023)

Add to Collection

Article Computer Science, Artificial Intelligence

Multimodal multi-objective optimization: Comparative study of the state-of-the-art

Wenhua Li et al.

Summary: Multimodal multi-objective problems (MMOPs) are common in the real world, where distant solutions in decision space have very similar objective values. To obtain more Pareto optimal solutions for MMOPs, many multimodal multi-objective evolutionary algorithms (MMEAs) have been proposed. However, there have been few studies comparing the performance of representative MMEAs. In this study, we review the related works and compare the performance of 15 state-of-the-art algorithms utilizing different diversity-maintaining techniques on various types of MMOPs, providing guidance for selecting/designing MMEAs in specific scenarios.

SWARM AND EVOLUTIONARY COMPUTATION (2023)

Add to Collection

Article Computer Science, Artificial Intelligence

A dynamic multi-objective particle swarm optimization algorithm based on adversarial decomposition and neighborhood evolution

Jinhua Zheng et al.

Summary: This paper proposes a dynamic multi-objective particle swarm optimization algorithm based on adversarial decomposition and neighborhood evolution (ADNEPSO). The algorithm utilizes the complementary characteristics in the search area of the adversarial vector and introduces a novel particle update strategy to enhance performance and adaptability to environmental changes.

SWARM AND EVOLUTIONARY COMPUTATION (2022)

Add to Collection

Article Computer Science, Artificial Intelligence

A clustering-based differential evolution algorithm for solving multimodal multi-objective optimization problems

Jing Liang et al.

Summary: This paper proposes a differential evolution algorithm based on clustering technique and elite selection mechanism to solve Multimodal Multi-objective Optimization Problems (MMOPs). The algorithm calculates comprehensive crowding degree and introduces elite selection mechanism to generate a well-distributed population, resulting in superior performance compared to other commonly used algorithms, as shown in extensive experiments on CEC'2019 benchmark functions.

SWARM AND EVOLUTIONARY COMPUTATION (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Weighted Indicator-Based Evolutionary Algorithm for Multimodal Multiobjective Optimization

Wenhua Li et al.

Summary: This study proposes an MMEA-WI algorithm based on a weighted indicator for solving multimodal multiobjective problems, which outperforms some state-of-the-art MMEAs in terms of performance metrics. By integrating diversity information and introducing a convergence archive, the algorithm effectively maintains diversity and ensures a better approximation of the Pareto-optimal front.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Multimodal Multiobjective Evolutionary Optimization With Dual Clustering in Decision and Objective Spaces

Qiuzhen Lin et al.

Summary: This article proposes a multimodal multiobjective evolutionary algorithm with dual clustering in decision and objective spaces to maintain diversity in solutions. Experimental results validate the advantages of this approach in maintaining diversity in both objective and decision spaces.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Differential evolution using improved crowding distance for multimodal multiobjective optimization

Caitong Yue et al.

Summary: This paper proposes a multimodal multiobjective differential evolution algorithm to solve the problem of many-to-one mappings in multiobjective optimization. The proposed method takes into account the diversity in both decision and objective space, and changes the way of calculating crowding distance to improve solution diversity.

SWARM AND EVOLUTIONARY COMPUTATION (2021)

Add to Collection

Article Automation & Control Systems

Evolutionary Multiobjective Blocking Lot-Streaming Flow Shop Scheduling With Machine Breakdowns

Yuyan Han et al.

IEEE TRANSACTIONS ON CYBERNETICS (2019)

Add to Collection

Article Computer Science, Artificial Intelligence

A novel scalable test problem suite for multimodal multiobjective optimization

Caitong Yue et al.

SWARM AND EVOLUTIONARY COMPUTATION (2019)

Add to Collection

Article Computer Science, Artificial Intelligence

A Scalable Indicator-Based Evolutionary Algorithm for Large-Scale Multiobjective Optimization

Wenjing Hong et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2019)

Add to Collection

Article Computer Science, Artificial Intelligence

A Multimodal Multiobjective Evolutionary Algorithm Using Two-Archive and Recombination Strategies

Yiping Liu et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2019)

Add to Collection

Article Computer Science, Artificial Intelligence

A Multiobjective Particle Swarm Optimizer Using Ring Topology for Solving Multimodal Multiobjective Problems

Caitong Yue et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2018)

Add to Collection

Editorial Material Computer Science, Artificial Intelligence

PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization

Ye Tian et al.

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE (2017)

Add to Collection

Article Computer Science, Artificial Intelligence

DMOEA-εC: Decomposition-Based Multiobjective Evolutionary Algorithm With the ε-Constraint Framework

Jie Chen et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2017)

Add to Collection

Article Computer Science, Artificial Intelligence

A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms

Joaquin Derrac et al.

SWARM AND EVOLUTIONARY COMPUTATION (2011)

Add to Collection

Article Engineering, Aerospace

Computing the Set of Epsilon-Efficient Solutions in Multiobjective Space Mission Design

Oliver Schuetze et al.

JOURNAL OF AEROSPACE COMPUTING INFORMATION AND COMMUNICATION (2011)

Add to Collection

Article Computer Science, Artificial Intelligence

Approximating the Set of Pareto-Optimal Solutions in Both the Decision and Objective Spaces by an Estimation of Distribution Algorithm

Aimin Zhou et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2009)

Add to Collection

Article Management

Omni-optimizer: A generic evolutionary algorithm for single and multi-objective optimization

Kalyanmoy Deb et al.

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2008)

Add to Collection

Article Computer Science, Artificial Intelligence

MOEA/D: A multiobjective evolutionary algorithm based on decomposition

Qingfu Zhang et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2007)

Add to Collection

Review Computer Science, Artificial Intelligence

Performance assessment of multiobjective optimizers: An analysis and review

E Zitzler et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2003)

Add to Collection

Article Computer Science, Artificial Intelligence

A fast and elitist multiobjective genetic algorithm: NSGA-II

K Deb et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2002)

Add to Collection

© Peeref 2019-2024. All rights reserved.