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A review on constraint handling techniques for population-based algorithms: from single-objective to multi-objective optimization
Most real-world problems involve some type of optimization problems that are often
constrained. Numerous researchers have investigated several techniques to deal with …
constrained. Numerous researchers have investigated several techniques to deal with …
A survey on home energy management
Energy is a vital resource for human activities and lifestyle, powering important everyday
infrastructures and services. Currently, pollutant and non-renewable sources, such as fossil …
infrastructures and services. Currently, pollutant and non-renewable sources, such as fossil …
Constraint-handling in nature-inspired numerical optimization: past, present and future
In their original versions, nature-inspired search algorithms such as evolutionary algorithms
and those based on swarm intelligence, lack a mechanism to deal with the constraints of a …
and those based on swarm intelligence, lack a mechanism to deal with the constraints of a …
Optimization of fused deposition modelling (FDM) process parameters using bacterial foraging technique
Fused deposition modelling (FDM) is a fast growing rapid prototy** (RP) technology due
to its ability to build functional parts having complex geometrical shapes in reasonable build …
to its ability to build functional parts having complex geometrical shapes in reasonable build …
Differential evolution in constrained numerical optimization: an empirical study
Motivated by the recent success of diverse approaches based on differential evolution (DE)
to solve constrained numerical optimization problems, in this paper, the performance of this …
to solve constrained numerical optimization problems, in this paper, the performance of this …
[كتاب][B] Evolutionary algorithms
A Pétrowski, S Ben-Hamida - 2017 - books.google.com
Evolutionary algorithms are bio-inspired algorithms based on Darwin's theory of evolution.
They are expected to provide non-optimal but good quality solutions to problems whose …
They are expected to provide non-optimal but good quality solutions to problems whose …
Push and pull search embedded in an M2M framework for solving constrained multi-objective optimization problems
In dealing with constrained multi-objective optimization problems (CMOPs), a key issue of
multi-objective evolutionary algorithms (MOEAs) is to balance the convergence and diversity …
multi-objective evolutionary algorithms (MOEAs) is to balance the convergence and diversity …
[PDF][PDF] 进化高维多目标优化算法研究综述
刘建昌， **飞， 王洪海， **田军 - 控制与决策, 2018 - researchgate.net
(1. 东北大学信息科学与工程学院, 沈阳110004; 2. **人民解放军陆军炮兵防空兵学院雷达
工程系, 合肥230031) 摘要: 首先针对常规多目标优化算法求解高维多目标优化时面临的选择 …
工程系, 合肥230031) 摘要: 首先针对常规多目标优化算法求解高维多目标优化时面临的选择 …
A dynamic hybrid framework for constrained evolutionary optimization
Based on our previous work, this paper presents a dynamic hybrid framework, called DyHF,
for solving constrained optimization problems. This framework consists of two major steps …
for solving constrained optimization problems. This framework consists of two major steps …
Optimal operation of multi-reservoir hydropower systems using enhanced comprehensive learning particle swarm optimization
Metaheuristics are promising optimization algorithms for tackling reservoir-system operation.
Comprehensive learning particle swarm optimization (CLPSO) is a state-of-the-art …
Comprehensive learning particle swarm optimization (CLPSO) is a state-of-the-art …