A novel dynamic multiobjective optimization algorithm with hierarchical response system
In this article, a novel dynamic multiobjective optimization algorithm (DMOA) is proposed
based on a designed hierarchical response system (HRS). Named HRS-DMOA, the …
based on a designed hierarchical response system (HRS). Named HRS-DMOA, the …
Elitism-based transfer learning and diversity maintenance for dynamic multi-objective optimization
In handling dynamic multi-objective optimization problems (DMOPs), transfer learning driven
methods have received considerable attention for finding a high-quality initial population …
methods have received considerable attention for finding a high-quality initial population …
A cluster prediction strategy with the induced mutation for dynamic multi-objective optimization
Dynamic multi-objective optimization problems (DMOPs) are multi-objective optimization
problems in which at least one objective and/or related parameter vary over time. The …
problems in which at least one objective and/or related parameter vary over time. The …
A novel combinational response mechanism for dynamic multi-objective optimization
Z Aliniya, SH Khasteh - Expert Systems with Applications, 2023 - Elsevier
Many real-world multi-objective optimization problems are dynamic. These problems require
an optimization algorithm to quickly track optimal solutions after changing the environment …
an optimization algorithm to quickly track optimal solutions after changing the environment …
Dynamic constrained multi-objective optimization based on adaptive combinatorial response mechanism
Z Aliniya, SH Khasteh - Applied Soft Computing, 2024 - Elsevier
Abstracts In dynamic multi-objective optimization problems (DMOPs), objective functions,
problem parameters, and constraints may change over time. Mainly, DMOPs use response …
problem parameters, and constraints may change over time. Mainly, DMOPs use response …
[HTML][HTML] Solving dynamic multi-objective optimization problems via quantifying intensity of environment changes and ensemble learning-based prediction strategies
Z Wang, L Xue, Y Guo, M Han, S Liang - Applied Soft Computing, 2024 - Elsevier
Algorithms designed to solve dynamic multi-objective optimization problems (DMOPs) need
to consider all of the multiple conflicting objectives to determine the optimal solutions …
to consider all of the multiple conflicting objectives to determine the optimal solutions …
A dynamic multiobjective evolutionary algorithm based on fine prediction strategy and nondominated solutions-guided evolution
P Wang, Y Ma - Applied Intelligence, 2023 - Springer
The dynamic multiobjective evolutionary algorithm (DMOEA) is an efficient solver for
dynamic multiobjective optimization problems (DMOPs). It is challenging for algorithms to …
dynamic multiobjective optimization problems (DMOPs). It is challenging for algorithms to …
Cheetah Optimizer for Multi-objective Optimization Problems
S Sharma, V Kumar - 2023 - researchsquare.com
In this paper, a new algorithm named multi-objective cheetah optimizer is presented for
solving multi-objective optimization problems. Cheetah optimizer is a new optimization …
solving multi-objective optimization problems. Cheetah optimizer is a new optimization …
The Effect of Individual Learning on the Improvement of the Quality of Training of the Staff of Eghtesade Novin Bank
S Mombani Abolfath, SZ Hosseini Doronkalaei… - Iranian journal of …, 2023 - iase-idje.ir
Purpose: Today, the quality of training plays an important role in improving the performance
of organizations. Therefore, the current research was conducted with the aim of determining …
of organizations. Therefore, the current research was conducted with the aim of determining …