Search

Now showing items 1-10 of 20

On the utilization of pair-potential energy functions in multi-objective optimization

Falcón-Cardona, J.G.; Covantes Osuna, E.; Coello, C.A.

; Ishibuchi, H. (2023-06-01)

In evolutionary multi-objective optimization (EMO), the pair-potential energy functions (PPFs) have been used to construct diversity-preserving mechanisms to improve Pareto front approximations. Despite PPFs have shown ...

An ACO-based Hyper-heuristic for Sequencing Many-objective Evolutionary Algorithms that Consider Different Ways to Incorporate the DM's Preferences

Rivera, G.; Cruz-Reyes, L.; Fernández, E.; Gomez-Santillan, C.; Rangel-Valdez, N.; Coello, C.A.

(2023-02-01)

Many-objective optimization is an area of interest common to researchers, professionals, and practitioners because of its real-world implications. Preference incorporation into Multi-Objective Evolutionary Algorithms (MOEAs) ...

Uncertainty-wise software anti-patterns detection: A possibilistic evolutionary machine learning approach

Boutaib, S.; Elarbi, M.; Bechikh, S.; Coello, C.A.

; Said, L. B. (2022-11-01)

Context: Code smells (a.k.a. anti-patterns) are manifestations of poor design solutions that can deteriorate software maintainability and evolution. Research gap: Existing works did not take into account the issue of ...

A convergence and diversity guided leader selection strategy for many-objective particle swarm optimization

Li, L.; Li, Y.; Lin, Q.; Ming, Z.; Coello, C.A.

(2022-10-01)

Recently, particle swarm optimizer (PSO) is extended to solve many-objective optimization problems (MaOPs) and becomes a hot research topic in the field of evolutionary computation. Particularly, the leader particle selection ...

On the Construction of Pareto-Compliant Combined Indicators

Falcón-Cardona, J.G.; Emmerich, M.; Coello, C.A.

(2022-08-12)

The most relevant property that a quality indicator (QI) is expected to have is Pareto compliance, which means that every time an approximation set strictly dominates another in a Pareto sense, the indicator must reflect ...

Multiple source transfer learning for dynamic multiobjective optimization

Ye, Y.; Lin, Q.; Ma, L.; Wong, K. C.; Gong, M.; Coello, C.A.

(2022-08-01)

Recently, dynamic multiobjective evolutionary algorithms (DMOEAs) with transfer learning have become popular for solving dynamic multiobjective optimization problems (DMOPs), as the used transfer learning methods in DMOEAs ...

An Ensemble Surrogate-Based Framework for Expensive Multiobjective Evolutionary Optimization

Lin, Q.; Wu, X.; Ma, L.; Li, J.; Gong, M.; Coello, C.A.

(2022-08-01)

Surrogate-assisted evolutionary algorithms (SAEAs) have become very popular for tackling computationally expensive multiobjective optimization problems (EMOPs), as the surrogate models in SAEAs can approximate EMOPs well, ...

A self-organizing weighted optimization based framework for large-scale multi-objective optimization

Li, Y.; Li, L.; Lin, Q.; Wong, K. C.; Ming, Z.; Coello, C.A.

(2022-07-01)

The solving of large-scale multi-objective optimization problem (LSMOP) has become a hot research topic in evolutionary computation. To better solve this problem, this paper proposes a self-organizing weighted optimization ...

Preference incorporation in MOEA/D using an outranking approach with imprecise model parameters

Fernández, E.; Rangel-Valdez, N.; Cruz-Reyes, L.; Gomez-Santillan, C.; Coello, C.A.

(2022-07-01)

Multi-objective Optimization Evolutionary Algorithms (MOEAs) face numerous challenges when they are used to solve Many-objective Optimization Problems (MaOPs). Decomposition-based strategies, such as MOEA/D, divide an MaOP ...

A dynamic multi-objective evolutionary algorithm based on polynomial regression and adaptive clustering

Yu, Q.; Lin, Q.; Zhu, Z.; Wong, K. C.; Coello, C.A.

(2022-06-01)

In this paper, a dynamic multi-objective evolutionary algorithm is proposed based on polynomial regression and adaptive clustering, called DMOEA-PRAC. As the Pareto-optimal solutions and fronts of dynamic multi-objective ...