Swarm intelligence: past, present and future

Yang, Xin‐She; Deb, Suash; Zhao, Yuxin; Fong, Simon; He, X. Q.

doi:10.1007/s00500-017-2810-5

Cited by 110 publications

(73 citation statements)

References 58 publications

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“…Γ is the Gamma function, while 1 ă λ ď 3 is a parameter. One of the advantages of using Lévy flights is that it has a small probability of long jumps, which enables the algorithm to escape from any local optima and thus increases its exploration capability (Yang et al, 2018;Viswanathan et al, 1999). The local search is mainly carried out by…”

Section: Cuckoo Searchmentioning

confidence: 99%

“…Such nonlinearity and multimodality can cause difficulties in solving these optimization problems. Both empirical observations and numerical simulations suggest that the final solution may depend on the initial starting points for multimodal optimization problems (Yang et al, 2018;Eskandar et al, 2012). This is especially true for gradient-based methods.…”

Section: Introductionmentioning

confidence: 99%

“…A good alternative is to use metaheuristic optimization algorithms, such as particle swarm optimization (PSO) and cuckoo search (CS). These metaheuristic optimizers are gradient-free optimizers, which do not require any prior knowledge or rigorous mathematical properties, such as continuity and smoothness (Yang et al, 2018;Li et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…In the past decade, various studies have shown that these metaheuristic algorithms are effective in solving different types of optimization problems, including noisy and dynamic problems (Yang et al, 2018;Fan et al, 2018;Cheng et al, 2018). For example, engineering design problems can be solved by an improved variant of the PSO (Isiet and Gadala, 2019) and the connectivity of the internet of things (IoT) can be enhanced by a multi-swarm optimization algorithm (Hasan and Al-Rizzo, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, we have used five different metaheuristic optimization algorithms for this study, and they are differential evolution (DE), particle swarm optimization (PSO), cuckoo search (CS), artificial bee colony (ABC) algorithm and genetic algorithm (GA). There are over 100 different algorithms and variants in the literature (Yang et al, 2018;Eskandar et al, 2012;Zaman et al, 2016), it is not possible to compare a good fraction of these algorithms. Therefore, the choice of algorithm has to focus on different search characteristics and representativeness of algorithms in the current literature.…”

Section: Introductionmentioning

confidence: 99%

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Influence of initialization on the performance of metaheuristic optimizers

Liu

Yang

2020

Applied Soft Computing

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All metaheuristic optimization algorithms require some initialization, and the initialization for such optimizers is usually carried out randomly. However, initialization can have some significant influence on the performance of such algorithms. This paper presents a systematic comparison of 22 different initialization methods on the convergence and accuracy of five optimizers: differential evolution (DE), particle swarm optimization (PSO), cuckoo search (CS), artificial bee colony (ABC) algorithm and genetic algorithm (GA). We have used 19 different test functions with different properties and modalities to compare the possible effects of initialization, population sizes and the numbers of iterations. Rigorous statistical ranking tests indicate that 43.37% of the functions using the DE algorithm show significant differences for different initialization methods, while 73.68% of the functions using both PSO and CS algorithms are significantly affected by different initialization methods. The simulations show that DE is less sensitive to initialization, while both PSO and CS are more sensitive to initialization. In addition, under the condition of the same maximum number of function evaluations (FEs), the population size can also have a strong effect. Particle swarm optimization usually requires a larger population, while the cuckoo search needs only a small population size. Differential evolution depends more heavily on the number of iterations, a relatively small population with more iterations can lead to better results. Furthermore, ABC is more sensitive to initialization, while such initialization has little effect on GA. Some probability distributions such as the beta distribution, exponential distribution and Rayleigh distribution can usually lead to better performance. The implications of this study and further research topics are also discussed in detail.

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Section: Cuckoo Searchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Influence of initialization on the performance of metaheuristic optimizers

Liu

Yang

2020

Applied Soft Computing

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Optimal PID controller of large‐scale PV farms for power systems LFO damping

Saadatmand

Mozafari

Gharehpetian

et al. 2020

Int Trans Electr Energ Syst

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Summary The penetration of large‐scale photovoltaic farms (LPFs) is ever increasing. Given that LPFs are added to power systems or replaced by conventional power plants, they should undertake the most common tasks of synchronous generators. One of these tasks is the low‐frequency oscillation (LFO) damping using power system stabilizer. This paper proposes a general method for LFO damping using LPFs by designing an optimal proportional‐integral‐derivative (PID) controller as a power oscillation damper in the nonlinear control loop of LPF. Considering the need for a valid LPF model, therefore the second generation generic model, developed and approved by western electricity coordinating council, is used. The PID controller is optimally tuned using the particle swarm optimization algorithm in order to produce an effective LFO damping. Finally, the performance of this controller is investigated in the modified two‐area test system, showing the proper performance of the LPF for LFO damping using the proposed optimal PID controller in the various operating condition and different short circuit ratio values.

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Influence of probability distribution initialization methods on the performance of advanced arithmetic optimization algorithm with application to unrelated parallel machine scheduling problem

Agushaka

Ezugwu

2022

Concurrency and Computation

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The article investigates the influence of several initialization methods on the performance of the newly proposed advanced arithmetic optimization algorithm, also known as the nAOA. The initialization conditions considered include population sizes, diversity of the population, and the number of iterations. We used 23 different probability distributions with different diversity to test the influence of initialization schemes on the convergence and accuracy of the nAOA. The benchmark classical test functions and the functions defined in the CEC 2020 suite having different properties and modalities were used to compare the possible effects of the initialization methods. The numerical results showed that the nAOA is sensitive to population size and the number of iterations, which must be large for optimal performance. Friedman's and correlation tests were used to gain useful insight into the results we obtained. Findings showed that the performance of the nAOA is not particularly sensitive to the different initialization schemes used for most test functions. The implication is that nAOA is relatively stable and robust. However, the variant of beta distribution, b(3,2), recorded the lowest mean rank for most functions. Hence, it is the best performing initialization distribution scheme for the test problems considered. We can also observe from the experimental results that 47.83% and 100% of the classical and CEC2020 test functions used in this article show significant differences for different initialization methods, respectively. We also investigated the applicability of the nAOA to solve the unrelated parallel machine scheduling problem with sequence‐dependent setup times by using the best‐identified set of initialization methods. The performance of the nAOA is evaluated by comparing its solution to seven other well‐known metaheuristic algorithms, and the results reveal that the nAOA can provide promising results in solving all problem instances of the problem under study using the best initialization schemes.

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Swarm intelligence: past, present and future

Cited by 110 publications

References 58 publications

Influence of initialization on the performance of metaheuristic optimizers

Influence of initialization on the performance of metaheuristic optimizers

Optimal PID controller of large‐scale PV farms for power systems LFO damping

Influence of probability distribution initialization methods on the performance of advanced arithmetic optimization algorithm with application to unrelated parallel machine scheduling problem

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