A novel differential evolution algorithm using local abstract convex underestimate strategy for global optimization

Zhou, Xiaogen; Zhang, Guijun; Xin, Hao; Yu, Li

doi:10.1016/j.cor.2016.05.015

Cited by 27 publications

(4 citation statements)

References 50 publications

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“…Due to the difference of MaxFEs between high-dimensional functions set and low-dimensional functions set, the results of mean FEs and SR under predefined accuracy level (Threshold) are divided into two tables. The results of mean FEs and SR are summarized in Tables S-8 x is the global optimum, the threshold is set to 1.00e-5 [90], except for f 07 and f 08 . The thresholds of f 07 and f 08 are set to 1.00e-2 and 1.00e-3, respectively.…”

Section: Comparison Of Convergence Speed and Srmentioning

confidence: 99%

“…The thresholds of f 07 and f 08 are set to 1.00e-2 and 1.00e-3, respectively. For low-dimensional functions, the threshold is set to 1.00e-5 [90], except for f 17 and f 19 , whose thresholds are 1.00e-3 and 1.00e-4, respectively. The stopping criterion is satisfied when the best fitness value reaches to the predefined threshold value or the number of FEs reaches to MaxFEs.…”

Section: Comparison Of Convergence Speed and Srmentioning

confidence: 99%

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Nuclear Reaction Optimization: A Novel and Powerful Physics-Based Algorithm for Global Optimization

et al. 2019

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Meta-heuristic algorithms have gained substantial popularity in recent decades and have focused on applications in a wide spectrum of fields. In this paper, a new and powerful physics-based algorithm named nuclear reaction optimization (NRO) is presented. Meanwhile, NRO imitates the nuclear reaction process and consists of two phases, namely, a nuclear fission (NFi) phase and a nuclear fusion (NFu) phase. The Gaussian walk and differential evolution operators between nucleus and neutron are employed for exploitation and appropriate exploration in the (NFi) phase, respectively. Meanwhile, the variants of differential evolution operator are utilized for exploration in the NFu phase, which consists of the ionization and fusion stages. Additionally, variants of Levy flight are used for random searching to escape from the local optima in each stage of NFu phase. The exploration and exploitation abilities of NRO can be balanced due to a combination of the two phases. Both constrained and unconstrained benchmark functions are employed for testing the performance of NRO. To make comparisons between NRO and the state-of-the-art algorithms, twenty-three classic benchmark functions and twenty-night modern benchmark functions are performed. Moreover, three engineering design optimization problems are solved as constrained benchmark functions by using NRO and the compared algorithms. The results illustrate that the proposed nuclear reaction optimization algorithm is a potential and powerful approach for global optimization.

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Section: Comparison Of Convergence Speed and Srmentioning

confidence: 99%

Section: Comparison Of Convergence Speed and Srmentioning

confidence: 99%

Nuclear Reaction Optimization: A Novel and Powerful Physics-Based Algorithm for Global Optimization

et al. 2019

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“…DE and its variants stand out as very competitive optimizers that have been successfully used to solve many real-world engineering problems [22]. DE is known for its simple structure, ease of use, robustness, and speed [23]. Many attempts have been made to improve DE's performance, two recent and efficient ones being Success-History based parameter Adaptation DE (SHADE) [24] and its improved variant L-SHADE [25].…”

Section: Shade and L-shade Algorithmsmentioning

confidence: 99%

Optimization of Lpda Excitations and the PBM Antenna Benchmarks Using Shade and L-Shade Algorithms

Formato¹,

Omran²

2017

PIER B

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The SHADE and L-SHADE variants of the Differential Evolution global search and optimization algorithm are used to compute optimized excitations for a Log Periodic Dipole Array antenna and to numerically solve the Pantoja-Bretones-Martin suite of antenna benchmark problems. Comparison to published data shows that SHADE and L-SHADE both are effective and efficient algorithms for solving the array excitation problem and the Pantoja-Bretones-Martin wire antenna benchmarks. L-SHADE clearly is more efficient on the array problem, but overall on the benchmarks the opposite is true, albeit to a lesser degree. The data support the view that neither algorithm is generally better than the other for the type of wire antenna problems considered here. Rather, which algorithm is more efficient is highly dependent on the specific antenna being optimized. In terms of the quality of their solutions, however, both algorithms accurately return the benchmarks' known global optima while both converge on different optimal array excitations that result in very similar objective function fitnesses.

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“…Differential evolution (DE), which was first proposed by Storn and Price (1997), is one of the most powerful evolutionary algorithms for global numerical optimization. The advantages of DE are its ease of use, simple structure, speed, efficiency and robustness (Wei et al, 2015;Zhou et al, 2016). Recently, DE has been successfully applied in diverse domains (Rajesh et al, 2016;Malathy et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

A multi-objective multicast routing optimization based on differential evolution in MANET

Wei

Qin

Cai

2018

IJICC

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Purpose The purpose of this paper is to propose a multi-objective differential evolution algorithm named as MOMR-DE to resolve multicast routing problem. In mobile ad hoc network (MANET), multicast routing is a non-deterministic polynomial -complete problem that deals with the various objectives and constraints. Quality of service (QoS) in the multicast routing problem mainly depends on cost, delay, jitter and bandwidth. So the cost, delay, jitter and bandwidth are always considered as multi-objective for designing multicast routing protocols. However, mobile node battery energy is finite and the network lifetime depends on node battery energy. If the battery power consumption is high in any one of the nodes, the chances of network’s life reduction due to path breaks are also more. On the other hand, node’s battery energy had to be consumed to guarantee high-level QoS in multicast routing to transmit correct data anywhere and at any time. Hence, the network lifetime should be considered as one objective of the multi-objective in the multicast routing problem. Design/methodology/approach Recently, many metaheuristic algorithms formulate the multicast routing problem as a single-objective problem, although it obviously is a multi-objective optimization problem. In the MOMR-DE, the network lifetime, cost, delay, jitter and bandwidth are considered as five objectives. Furthermore, three QoS constraints which are maximum allowed delay, maximum allowed jitter and minimum requested bandwidth are included. In addition, we modify the crossover and mutation operators to build the shortest-path multicast tree to maximize network lifetime and bandwidth, minimize cost, delay and jitter. Findings Two sets of experiments are conducted and compared with other algorithms for these problems. The simulation results show that our proposed method is capable of achieving faster convergence and is more preferable for multicast routing in MANET. Originality/value In MANET, most metaheuristic algorithms formulate the multicast routing problem as a single-objective problem. However, this paper proposes a multi-objective differential evolution algorithm to resolve multicast routing problem, and the proposed algorithm is capable of achieving faster convergence and more preferable for multicast routing.

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A novel differential evolution algorithm using local abstract convex underestimate strategy for global optimization

Cited by 27 publications

References 50 publications

Nuclear Reaction Optimization: A Novel and Powerful Physics-Based Algorithm for Global Optimization

Nuclear Reaction Optimization: A Novel and Powerful Physics-Based Algorithm for Global Optimization

Optimization of Lpda Excitations and the PBM Antenna Benchmarks Using Shade and L-Shade Algorithms

A multi-objective multicast routing optimization based on differential evolution in MANET

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