Adaptive Estimation Distribution Distributed Differential Evolution for Multimodal Optimization Problems

Wang, Zi-Jia; Zhou, Yuejiao; Zhang, Mengjie

doi:10.1109/tcyb.2020.3038694

Cited by 40 publications

(7 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In DMS-L-PSO [61] and SHADE-ILS [82] (the winner of the CEC2018 large-scale competition), they both use the BFGS local search technique. In some multimodal optimization algorithms [17][18], the local search technique is conducted by sampling some points around the best individual using Gaussian distribution. Herein, we compare GTDE with its variants using different local search techniques to verify the efficiency of the GT-based modification.…”

Section: G Effects Of the Gtde Componentsmentioning

confidence: 99%

Gene Targeting Differential Evolution: A Simple and Efficient Method for Large-Scale Optimization

Wang

Jian

Zhan

et al. 2023

IEEE Trans. Evol. Computat.

Self Cite

View full text Add to dashboard Cite

Large-scale optimization problems (LSOPs) are challenging because the algorithm is difficult in balancing too many dimensions and in escaping from trapped bottleneck dimensions. To improve solutions, this paper introduces targeted modification to the certain values in the bottleneck dimensions. Analogous to gene targeting (GT) in biotechnology, we experiment on targeting the specific genes in candidate solution to improve its trait in differential evolution (DE). We propose a simple and efficient method, called GT-based DE (GTDE), to solve LSOPs. In the algorithm design, a simple GT-based modification is developed to perform on the best individual, comprising probabilistically targeting the location of bottleneck dimensions, constructing a homologous targeting vector, and inserting the targeting vector into the best individual. In this way, all the bottleneck dimensions of the best individual can be probabilistically targeted and modified to break the bottleneck and to provide global guidance for more optimal evolution. Note that the GT is only performed on the globally best individual and is just carried out as a simple operator that is added to the standard DE. Experimental studies compare the GTDE with some other state-of-the-art large-scale optimization algorithms, including the winners of CEC2010, CEC2012, CEC2013, and CEC2018 competitions on large-scale optimization. The results show that the GTDE is efficient and performs better than or at least comparable to the others in solving LSOPs.Manuscript received….revised….accepted….

show abstract

Section: G Effects Of the Gtde Componentsmentioning

confidence: 99%

Gene Targeting Differential Evolution: A Simple and Efficient Method for Large-Scale Optimization

Wang

Jian

Zhan

et al. 2023

IEEE Trans. Evol. Computat.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Lin C et al introduced the concept of grouping into the DE to improve its local search ability [35]. Wang et al proposed a distributed differential evolution (DDE) algorithm, which is called AED-DDE, for solving MMOPs [36]. Liu et al proposed a hybrid DE algorithm based on the lion swarm optimization [37].…”

Section: Related Workmentioning

confidence: 99%

Path Planning of Mobile Robots Based on An Improved Particle Swarm Optimization Algorithm

Yuan

Sun

2022

Preprint

View full text Add to dashboard Cite

Aiming at disadvantages of particle swarm optimization in the path planning of mobile robots, such as low convergence accuracy and easy maturity, this paper proposes an improved particle swarm optimization algorithm based on differential evolution. First, the concept of corporate governance is introduced, adding adaptive adjustment weights and acceleration coefficients to improve the traditional particle swarm optimization and increase the algorithm convergence speed. Then, in order to improve the performance of differential evolution algorithm, the size of the mutation is controlled by adding adaptive parameters. Moreover, a "high-intensity training" mode is developed to use the improved differential evolution algorithm to intensively train the global optimal position of the particle swarm optimization, which can improve the search precision of the algorithm. Finally, the mathematical model for robot path planning is devised as a two-objective optimization with two indices, i.e., the path length and the degree of danger to optimize the path planning. The proposed algorithm is applied to different experiments for path planning simulation tests. The results demonstrate the feasibility and effectiveness of it in solving mobile robot path planning problem.

show abstract

“…Additionally, a dynamic dual-learning (DDL) mutation strategy is developed based on two wellknown differential evolution (DE) operators, DE/currentto-rand/1 and DE/current-to-best/1 [21] , to balance the diversity and convergence of the two tasks at different stages of evolution. DE proposed by storn and price [22] , as a simple yet powerful method, is chosen because of its successful application in a large number of optimization problems [23,24] . Finally, the proposed algorithm is verified using an example of a coal mine located in Shanxi.…”

Section: Introductionmentioning

confidence: 99%

A Constraint Adaptive Multi-Tasking Differential Evolution Algorithm: Designed for Dispatch of Integrated Energy System in Coal Mine

Dai,

Sun,

et al. 2024

Tsinghua Sci. Technol.

View full text Add to dashboard Cite

The dispatch of integrated energy systems in coal mines (IES-CM) with mine-associated supplies is vital for efficient energy utilization and carbon emissions reduction. However, IES-CM dispatch is highly challenging due to its feature as multi-objective and strong multi-constraint. Existing constrained multi-objective evolutionary algorithms often fall into locally feasible domains with poorly distributed Pareto front, which greatly deteriorates dispatch performance. To tackle this problem, we transform the traditional dispatch model of IES-CM into two tasks: the main task with all constraints and the helper task with constraint adaptive. Then we propose a constraint adaptive multi-tasking differential evolution algorithm (CA-MTDE) to optimize these two tasks effectively. The helper task with constraint adaptive is developed to obtain infeasible solutions near the feasible domain. The purpose of this infeasible solution is to transfer guiding knowledge to help the main task move away from local search. Additionally, a dynamic dual-learning strategy using DE/current-to-rand/1 and DE/current-to-best/1 is developed to maintain task diversity and convergence. Finally, we comprehensively evaluate the performance of CA-MTDE by applying it to a coal mine in Shanxi Province, considering two IES-CM scenarios. Results demonstrate the feasibility of CA-MTDE and its ability to generate a Pareto front with exceptional convergence, diversity, and distribution.

show abstract

Adaptive Estimation Distribution Distributed Differential Evolution for Multimodal Optimization Problems

Cited by 40 publications

References 45 publications

Gene Targeting Differential Evolution: A Simple and Efficient Method for Large-Scale Optimization

Gene Targeting Differential Evolution: A Simple and Efficient Method for Large-Scale Optimization

Path Planning of Mobile Robots Based on An Improved Particle Swarm Optimization Algorithm

A Constraint Adaptive Multi-Tasking Differential Evolution Algorithm: Designed for Dispatch of Integrated Energy System in Coal Mine

Contact Info

Product

Resources

About