DE-RCO: Rotating Crossover Operator With Multiangle Searching Strategy for Adaptive Differential Evolution

Deng, Libao; Wang, Sha; Qiao, Liyan; Zhang, Baoquan

doi:10.1109/access.2017.2786347

Cited by 32 publications

(9 citation statements)

References 31 publications

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“…In EsDEr-NR [22], the niching-based population reduction method was employed to determine the number of population in each generation. As for F and Cr, many significant developments have also been performed, such as the invention of jDE [17], JADE [16], and DE-RCO [34]. Table 1 lists six improved DE algorithms.…”

Section: ) Selectionmentioning

confidence: 99%

“…One one hand, focusing on one parameter can help to provide insights into understanding DE algorithms. On the other hand, new advances in tuning a single parameter can be plugged into existed DE algorithms to further improve performance, as is evident in [29] and [34]. Note that F is the unique feature of DE algorithms, as compared with NP and Cr.…”

Section: ) Selectionmentioning

confidence: 99%

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Correlations Between the Scaling Factor and Fitness Values in Differential Evolution

2020

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Designing fitness-based adaptive scaling factor (F) is an effective method to enhance the performance of differential evolution (DE) algorithms. This paper investigates the correlations between F and fitness values of target vectors, base vectors and difference vectors. The correlations are described by the notations of monotonicity and nonlinearity. Monotonicity is used to examine whether the optimization performance of DE and the fitness values of certain vectors have positive or negative correlation. Nonlinearity denotes the operation in which nonlinear mappings are used to redistribute the values of F in [0, 1] so as to boost the optimization performance. These two aspects of correlations are empirically tested on the Numerical Optimization Competition benchmark functions in IEEE Congress on Evolutionary Computation. Simulation results reveal different qualitative and quantitative correlations between F and fitness values of different vectors. Then, a new F that combines these relations is designed. Its strength is numerically verified by testing different CEC Benchmark functions.

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Section: ) Selectionmentioning

confidence: 99%

Section: ) Selectionmentioning

confidence: 99%

Correlations Between the Scaling Factor and Fitness Values in Differential Evolution

2020

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“…DE is a well-known IOAs with strong robustness. According to the distance and direction information between individuals in the current population, it guides the population to search for the optimal solution through differential calculation [6]. The mutation strategy of DE has excellent global search ability, which is adopted by many other improved algorithms to enhance the optimization performance [23]- [25].…”

Section: A Random-scaled Differential Mutationmentioning

confidence: 99%

“…The associate editor coordinating the review of this manuscript and approving it for publication was Jagdish Chand Bansal. Artificial Bee Colony (ABC) [5], Differential Evolution (DE) [6], [7], Gray Wolf Optimizer (GWO) [8], Flower Pollination Algorithm (FPA) [9], Cuckoo Search (CS) [10], Biogeography-Based Optimization (BBO) [11] and so forth.…”

Section: Introductionmentioning

confidence: 99%

Improved Biogeography-Based Optimization Algorithm and Its Application to Clustering Optimization and Medical Image Segmentation

2019

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In order to improve the optimization efficiency of the biogeography-based optimization (BBO) algorithm, an improved BBO algorithm, that is, worst opposition learning and random-scaled differential mutation BBO (WRBBO), is presented in this paper. First, BBO's mutation operator is deleted to reduce the computational complexity and a more efficient random-scaled differential mutation operator is merged into BBO's migration operator to obtain global search ability. Second, in order to balance exploration and exploitation, the BBO's migration operator is replaced with a dynamic heuristic crossover to enhance the local search ability. Finally, a worst opposition learning is merged into the improved algorithm to avoid trapping into local optima. A large number of experiments are made on 18 various kinds of classic benchmark functions and some complex functions from the CEC-2013 test set. In addition, WRBBO is applied to clustering optimization and medical image segmentation. The experimental results show that WRBBO has better optimization efficiency on benchmark function optimization, clustering optimization, and medical image segmentation than quite a few state-of-the-art BBO variants and other algorithms.

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“…Biogeography-based optimization (BBO) [12] is one of the intelligent optimization algorithms, such as other optimization algorithms (particle swarm optimization (PSO) [13], artificial bee colony (ABC) [14], ant colony optimization (ACO) [15], differential evolution (DE) [16,17], Grey Wolf Optimizer (GWO) [18], monarch butterfly optimization (MBO) [19], earthworm optimization algorithm (EWA) [20], elephant herding optimization (EHO) [21], moth search (MS) algorithm [22], slime mould algorithm (SMA) [23], and Harris hawks optimization (HHO) [24]); a fixed calculation mode is constructed to solve diverse optimization problems. Gaining-sharing knowledge optimization algorithm (GSK) [25] was proposed, which is based on the concept of acquiring and sharing knowledge in the human life cycle.…”

Section: Introductionmentioning

confidence: 99%

Improved Biogeography-Based Optimization Algorithm by Hierarchical Tissue-Like P System with Triggering Ablation Rules

Sang

Liu

Zhang

et al. 2021

Mathematical Problems in Engineering

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BBO is one of the new metaheuristic optimization algorithms, which is based on the science of biogeography. It can be used to solve optimization problems through the migration and drift of species between habitats. Many improved BBO algorithms have been proposed, but there were still many shortcomings in global optimization, convergence speed, and algorithm complexity. In response to the above problems, this paper proposes an improved BBO algorithm (DCGBBO) by hierarchical tissue-like P system with triggering ablation rules. Membrane computing is a branch of natural computing that aims to abstract computational models (P system) from the structure and function of biological cells and from the collaboration of cell groups such as organs and tissues. In this paper, firstly, a dynamic crossover migration operator is generated to improve the global search ability and also increase the species diversity. Secondly, a dynamic Gaussian mutation operator is introduced to speed up convergence and improve local search capabilities. To guarantee the correctness and feasibility of the mutation, a unified maximum mutation rate is designed. Finally, a hierarchical tissue-like P system with triggering ablation rules is combined with the DCGBBO algorithm, making use of evolution rules and communication rules to achieve migration and mutation of solutions and reduce computational complexity. In the experiments, eight classic benchmark functions and CEC 2017 benchmark functions are applied to demonstrate the effect of our algorithm. We apply the proposed algorithm to segment four colour pictures, and the results proved to be better compared to other algorithms.

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DE-RCO: Rotating Crossover Operator With Multiangle Searching Strategy for Adaptive Differential Evolution

Cited by 32 publications

References 31 publications

Correlations Between the Scaling Factor and Fitness Values in Differential Evolution

Correlations Between the Scaling Factor and Fitness Values in Differential Evolution

Improved Biogeography-Based Optimization Algorithm and Its Application to Clustering Optimization and Medical Image Segmentation

Improved Biogeography-Based Optimization Algorithm by Hierarchical Tissue-Like P System with Triggering Ablation Rules

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