Ensemble prediction-based dynamic robust multi-objective optimization methods

Guo, Yinan; Yang, Huan; Chen, Meirong; Cheng, Jian; Gong, Dunwei

doi:10.1016/j.swevo.2019.03.015

Cited by 112 publications

(39 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Three metrics are employed to analyze the performance of the schemes. The inverted generational distance (IGD) [29] calculates the distance between the objective values of the Pareto optimal solutions and the true Pareto front.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Dynamic Multiobjective Software Project Scheduling Optimization Method Based on Firework Algorithm

Cheng

Guo

et al. 2019

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

Software project scheduling is essentially a kind of project scheduling problem with limited human resources. During the development process of a software product, reworking the completed projects, reassessing the workload, and changing the number of employees or their skills are the frequently occurring dynamic issues having direct influences on designing a scheduling scheme. Taking the development cost and duration, the robustness, and the stability of the scheduling schemes as the objective functions, software project scheduling is modeled as a dynamic four-objective optimization problem. The various programming habits among the employees form the specific constraints for the reworking tasks, and the skills of the employees vary due to the effects of learning and forgetting. To solve this problem, an improved multiobjective firework algorithm with a novel explosion operator and reservation strategy is incorporated with rescheduling methods to fully guide the evolution by using the historical evolutionary knowledge. The experimental results indicate that the proposed method has better scheduling performance, and the optimal scheduling schemes have better robustness and stability.

show abstract

Section: Experimental Results and Analysismentioning

confidence: 99%

Dynamic Multiobjective Software Project Scheduling Optimization Method Based on Firework Algorithm

Cheng

Guo

et al. 2019

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…If X i is a producer (29) Producers: update the position X i of birds using equation 5; (30) Else (31) Scroungers: update the position X i of birds using equation (6 6 Computational Intelligence and Neuroscience…”

Section: N);mentioning

confidence: 99%

“…Dynamic multi-swarm method has been widely used in real-world applications, because it is efficient and easy to implement. In addition, it is very common in the improvement of swarm intelligent optimization, such as coevolutionary algorithm [ 26 ], the framework of evolutionary algorithms [ 27 ], multiobjective particle swarm optimization [ 28 ], hybrid dynamic robust [ 29 ], and PSO algorithm [ 30 , 31 ]. However, the PSO algorithm is easy to fall into the local optimum and its generalization performance is not high.…”

Section: Bird Swarm Algorithm and Its Improvementmentioning

confidence: 99%

Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest Classification Model

Zhang

Xia

et al. 2020

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Bird swarm algorithm is one of the swarm intelligence algorithms proposed recently. However, the original bird swarm algorithm has some drawbacks, such as easy to fall into local optimum and slow convergence speed. To overcome these short-comings, a dynamic multi-swarm differential learning quantum bird swarm algorithm which combines three hybrid strategies was established. First, establishing a dynamic multi-swarm bird swarm algorithm and the differential evolution strategy was adopted to enhance the randomness of the foraging behavior’s movement, which can make the bird swarm algorithm have a stronger global exploration capability. Next, quantum behavior was introduced into the bird swarm algorithm for more efficient search solution space. Then, the improved bird swarm algorithm is used to optimize the number of decision trees and the number of predictor variables on the random forest classification model. In the experiment, the 18 benchmark functions, 30 CEC2014 functions, and the 8 UCI datasets are tested to show that the improved algorithm and model are very competitive and outperform the other algorithms and models. Finally, the effective random forest classification model was applied to actual oil logging prediction. As the experimental results show, the three strategies can significantly boost the performance of the bird swarm algorithm and the proposed learning scheme can guarantee a more stable random forest classification model with higher accuracy and efficiency compared to others.

show abstract

“…However, weights cannot be utilized as the criteria to distinguish the first category from the second category, since a series of multi-objective algorithms also utilize weights to determine a complete Pareto front, such as: the multiobjective bat algorithm [33], which utilizes K randomly chosen weights to solve K-objective problems; the multiobjective evolutionary algorithm based on decomposition [34], [35], which decomposes a MOP into several subproblems and solve them by aggregation, including the weighted sum approach, the Tchebycheff approach and the boundary intersection approach; the multi-objective evolutionary algorithm based on decomposition with adaptive replacement strategies [36], which utilizes a sigmoid function to adaptively adjust replaced neighbors of individuals in MOEA/D; the multi-objective evolutionary algorithm based on decomposition with composite operator selection [35], which introduces four types of cross-mutate operations for evolutionary computations; the multi-objective crow search algorithm [37], which utilizes a set of determined weight vectors and employees the max-min strategy; All of them are trying to search for a significant Pareto front while applying weights. To guarantee the robustness of different multi-objective optimization algorithms and solve the dynamic multi-objective optimization problems [38], prediction models such as moving average, autoregressive and single exponential smoothing can also be aggregate with weights to achieve this goal. Since these multi-objective optimization algorithms do not aggregate the MOPs into single-objective optimization problems to find out a global best solution, they all belong to the second category.…”

Section: Related Workmentioning

confidence: 99%

A Vascular Invasive Tumor Growth Optimization Algorithm for Multi-Objective Optimization

et al. 2020

View full text Add to dashboard Cite

Multi-objective optimization problems (MOPs) have received much attention in recent years. To deal with these problems, many multi-objective optimization algorithms have been proposed, especially the heuristic algorithms. In this paper, we proposed a multi-objective optimization algorithm called vascular invasive tumor growth optimization (VITGO), which based on the invasive tumor growth optimization and utilized a vascular mechanism to solve the MOPs. The newly proposed algorithm contains two parts: the vascular units and tumor cells. The former ones are utilized to record the Pareto solutions of the MOPs and define the search direction, and the latter ones are utilized to cooperate with vascular units to search deeper and wider. The mechanisms in the VITGO algorithm includes: endpoint generation, approximate Pareto front guidance, opposite searching, and adaptively detailed searching. Experiments showed that compared with other state-of-the-art multi-objective optimization algorithms, VITGO performs better in convergence and diversity.

show abstract

Ensemble prediction-based dynamic robust multi-objective optimization methods

Cited by 112 publications

References 32 publications

Dynamic Multiobjective Software Project Scheduling Optimization Method Based on Firework Algorithm

Dynamic Multiobjective Software Project Scheduling Optimization Method Based on Firework Algorithm

Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest Classification Model

A Vascular Invasive Tumor Growth Optimization Algorithm for Multi-Objective Optimization

Contact Info

Product

Resources

About