Search Results for author: Miqing Li

Found 20 papers, 8 papers with code

A First Running Time Analysis of the Strength Pareto Evolutionary Algorithm 2 (SPEA2)

no code implementations23 Jun 2024 Shengjie Ren, Chao Bian, Miqing Li, Chao Qian

Evolutionary algorithms (EAs) have emerged as a predominant approach for addressing multi-objective optimization problems.

2k Evolutionary Algorithms

Maintaining Diversity Provably Helps in Evolutionary Multimodal Optimization

no code implementations4 Jun 2024 Shengjie Ren, Zhijia Qiu, Chao Bian, Miqing Li, Chao Qian

In the real world, there exist a class of optimization problems that multiple (local) optimal solutions in the solution space correspond to a single point in the objective space.

Diversity Evolutionary Algorithms

An Archive Can Bring Provable Speed-ups in Multi-Objective Evolutionary Algorithms

no code implementations4 Jun 2024 Chao Bian, Shengjie Ren, Miqing Li, Chao Qian

In this paper, we theoretically show, for the first time, that using an archive can guarantee speed-ups for MOEAs.

Evolutionary Algorithms

Adapting Multi-objectivized Software Configuration Tuning

1 code implementation6 Apr 2024 Tao Chen, Miqing Li

Experiments on several real-world systems, objectives, and budgets show that, for 71% of the cases, AdMMO is considerably superior to MMO and a wide range of state-of-the-art optimizers while achieving generally better efficiency with the best speedup between 2. 2x and 20x.

Non-Elitist Evolutionary Multi-Objective Optimisation: Proof-of-Principle Results

no code implementations26 May 2023 Zimin Liang, Miqing Li, Per Kristian Lehre

Elitism, which constructs the new population by preserving best solutions out of the old population and newly-generated solutions, has been a default way for population update since its introduction into multi-objective evolutionary algorithms (MOEAs) in the late 1990s.

Diversity Evolutionary Algorithms

Multi-Objective Archiving

no code implementations16 Mar 2023 Miqing Li, Manuel López-Ibáñez, Xin Yao

Such an archive can be solely used to store high-quality solutions presented to the decision maker, but in many cases may participate in the search process (e. g., as the population in evolutionary computation).

Do Performance Aspirations Matter for Guiding Software Configuration Tuning?

1 code implementation9 Jan 2023 Tao Chen, Miqing Li

Before we design better optimizers, a crucial engineering decision to make therein is how to handle the performance requirements with clear aspirations in the tuning process.

Decision Making

Multi-agent Dynamic Algorithm Configuration

1 code implementation13 Oct 2022 Ke Xue, Jiacheng Xu, Lei Yuan, Miqing Li, Chao Qian, Zongzhang Zhang, Yang Yu

MA-DAC formulates the dynamic configuration of a complex algorithm with multiple types of hyperparameters as a contextual multi-agent Markov decision process and solves it by a cooperative multi-agent RL (MARL) algorithm.

Multi-Armed Bandits Reinforcement Learning (RL)

An Effective and Efficient Evolutionary Algorithm for Many-Objective Optimization

no code implementations31 May 2022 Yani Xue, Miqing Li, Xiaohui Liu

In such problems, classic Pareto-based algorithms fail to provide sufficient selection pressure towards the Pareto front, whilst recently developed algorithms, such as decomposition-based ones, may struggle to maintain a set of well-distributed solutions on certain problems (e. g., those with irregular Pareto fronts).

Density Estimation Evolutionary Algorithms +1

The Weights can be Harmful: Pareto Search versus Weighted Search in Multi-Objective Search-Based Software Engineering

1 code implementation8 Feb 2022 Tao Chen, Miqing Li

However, when clear preferences of the stakeholders (e. g., a set of weights which reflect relative importance between objectives) are available prior to the search, weighted search is believed to be the first choice since it simplifies the search via converting the original multi-objective problem into a single-objective one and enable the search to focus on what only the stakeholders are interested in.

MMO: Meta Multi-Objectivization for Software Configuration Tuning

1 code implementation14 Dec 2021 Pengzhou Chen, Tao Chen, Miqing Li

We also demonstrate that the MMO model with the new normalization can consolidate recent model-based tuning tools on 68% of the cases with up to 1. 22x speedup in general.

Multi-Objectivizing Software Configuration Tuning (for a single performance concern)

no code implementations31 May 2021 Tao Chen, Miqing Li

Automatically tuning software configuration for optimizing a single performance attribute (e. g., minimizing latency) is not trivial, due to the nature of the configuration systems (e. g., complex landscape and expensive measurement).


How to Evaluate Solutions in Pareto-based Search-Based Software Engineering? A Critical Review and Methodological Guidance

1 code implementation20 Feb 2020 Miqing Li, Tao Chen, Xin Yao

We then conduct an in-depth analysis of quality evaluation indicators/methods and general situations in SBSE, which, together with the identified issues, enables us to codify a methodological guidance for selecting and using evaluation methods in different SBSE scenarios.

Multiobjective Optimization

Search-Based Software Engineering for Self-Adaptive Systems: Survey, Disappointments, Suggestions and Opportunities

1 code implementation22 Jan 2020 Tao Chen, Miqing Li, Ke Li, Kalyanmoy Deb

In this paper, we provide the first systematic and comprehensive survey exclusively on SBSE for SASs, covering papers in 27 venues from 7 repositories, which eventually leads to several key statistics from the most notable 74 primary studies in this particular field of research.

Self Adaptive System

Multiobjective Test Problems with Degenerate Pareto Fronts

no code implementations7 Jun 2018 Liangli Zhen, Miqing Li, Ran Cheng, Dezhong Peng, Xin Yao

The redundancy of some objectives can lead to the multiobjective problem having a degenerate Pareto front, i. e., the dimension of the Pareto front of the $m$-objective problem be less than (m-1).

Multiobjective Optimization

What Weights Work for You? Adapting Weights for Any Pareto Front Shape in Decomposition-based Evolutionary Multi-Objective Optimisation

no code implementations8 Sep 2017 Miqing Li, Xin Yao

A set of weights distributed uniformly in a simplex often lead to a set of well-distributed solutions on a Pareto front with a simplex-like shape, but may fail on other Pareto front shapes.

How to Read Many-Objective Solution Sets in Parallel Coordinates

no code implementations30 Apr 2017 Miqing Li, Liangli Zhen, Xin Yao

In this paper, we make some observations of the parallel coordinates plot, in terms of comparing the quality of solution sets, understanding the shape and distribution of a solution set, and reflecting the relation between objectives.

Evolutionary Algorithms

Dominance Move: A Measure of Comparing Solution Sets in Multiobjective Optimization

no code implementations1 Feb 2017 Miqing Li, Xin Yao

In this paper, we propose a quality measure, called dominance move (DoM), to compare solution sets generated by multiobjective optimizers.

Decision Making Multiobjective Optimization

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