Parasitism – Predation algorithm (PPA): A novel approach for feature selection

Mohamed, Al-Attar Ali; Hassan, Somaia Awad; Hemeida, Ashraf Mohamed; Alkhalaf, Salem; Mahmoud, Mountasser M.M.; Eldin, Ayman M. Baha

doi:10.1016/j.asej.2019.10.004

Cited by 58 publications

(28 citation statements)

References 43 publications

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“…The cuckoo-cat-crow system is an element with its own tasks. The crow is the host, the cuckoo a parasite, and the cats a predator of the crow's nest [24].…”

Section: Parasitism -Predation Algo-rithmmentioning

confidence: 99%

An Improved Neural Network Based on Parasitism – Predation Algorithm for an Automatic Voltage Regulator

et al. 2021

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The parasitism – predation algorithm (PPA) is an optimization method that duplicates the interaction of mutualism between predators (cats), parasites (cuckoos), and hosts (crows). The study employs a combination of the PPA methods using the cascade-forward backpropagation neural network. This hybrid method employs an automatic voltage regulator (AVR) on a single machine system, with the performance measurement focusing on speed and the rotor angle. The performance of the proposed method is compared with the feed-forward backpropagation neural network (FFBNN), cascade-forward backpropagation neural network (CFBNN), Elman recurrent neural network (E-RNN), focused time-delay neural network (FTDNN), and distributed time-delay neural network (DTDNN). The results show that the proposed method exhibits the best speed and rotor angle performance. The PPA-CFBNN method has the ability to reduce the overshoot of the speed by 1.569% and the rotor angle by 0.724%.

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“…The cuckoo-cat-crow system is an element with its own tasks. The crow is the host, the cuckoo a parasite, and the cats a predator of the crow's nest [24].…”

Section: Parasitism -Predation Algo-rithmmentioning

confidence: 99%

An Improved Neural Network Based on Parasitism – Predation Algorithm for an Automatic Voltage Regulator

et al. 2021

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“…The PPA is a population-based optimization algorithm which mimics the crow-cuckoo-cat relation in nature. 54 The crow is treated as swarm, and the cuckoos parasitize their eggs in their nests nearby its eggs, in return, the cuckoo generates a smelly local defecation to save the crow children from a predator. On the other hand, the cat is a predator, it attacks the crow nest, the cuckoo faces the cat and protects the nest.…”

Section: Equivalent Model Of Sofcmentioning

confidence: 99%

“…Furthermore, the proposed algorithm uses an adaptive discovery rate and tracing mode with adaptive social learning and velocity limits to search in the neighborhood of the optimal global solution as a local search operator. 54 The key contributions of the presented article are as follows: The organization of the manuscript is listed as below: The circuit model of SOFC and modeling equations were described in Section 2, the implementation steps and theory behind the PPA are documented in Section 3. The proposed objective function is clarified in Section 4.…”

Section: Introductionmentioning

confidence: 99%

Estimating the optimal parameters of solid oxide fuel cell‐based circuit using parasitism‐predation algorithm

Yousri

Fathy

Babu

et al. 2021

Intl J of Energy Research

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The process of constructing a reliable mathematical model of solid oxide fuel cell (SOFC) is a challenge due to its complex nature. This paper proposes a new methodology incorporated a recent meta-heuristic algorithm named parasitism-predation algorithm (PPA) to estimate the optimal parameters of SOFC equivalent circuit. Two experiments are conducted in this work; the first one comprises four measured datasets for a commercial enhanced cylindrical SOFC manufactured by Siemen Energy. While the second series consists of five measured datasets for a theoretical 5 KW ð Þ dynamic SOFC stack with 96 connected cells. The collected datasets are measured at different operating conditions. An excessive comparative study is presented with other optimizers of comprehensive learning particle swarm optimization (CLPSO), improved PSO with difference mean with perturbation (DMP_PSO), heterogeneous CLPSO (HCLPSO), locally informed PSO (LIPS), modified CSO with tri-competitive mechanism (MCSO), opposition-based learning competitive PSO (OBLCPSO), ranking-based biased learning swarm optimizer (RBLSO), competitive swarm optimizer (CSO), hybrid Jaya with DE (JayaDE), and social learning PSO (SLPSO). Furthermore, statistical analyses of the ranking tests, multiple sign tests, Friedman tests, and ANOVA are performed. The obtained results confirmed the proposed PPA's competence in constructing a reliable model of SOFC as it provides the least mean square error (MSE) between the measured and estimated characteristics of 2.164e À6 in the first series of experiments at 1073 K, in contrast, the most peer (CLPSO) provides 5.57e À6 . Similarly, in the second series of experiments, PPA achieves lease MSE of 7.17e À2 at 973 K; meanwhile, the most peer (CLPSO) attains 5.44e À1 .

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“…Bees search for food and use pheromone to mark the path, guiding other individuals to find the shortest path from the hive to the food. Swarm Intelligent (SI)-based algorithms are inspired by the intelligent behavior of biological swarms, including Marine Predators Algorithm (MPA) [24], Seagull Optimization Algorithm (SOA) [25], Spotted Hyena Optimizer (SHO) [26], and Naked Mole-Rat algorithm (NMR) [27], Equilibrium Optimizer (EO) [28], Parasitism Predation Algorithm (PPA) [29], Manta ray foraging optimization (MRFO) [30], Social Ski-Driver optimization algorithm (SSD) [31], etc. For example, Ant colony algorithm (ACO) [32] takes ant colony as inspiration.…”

Section: Researchers Create New Meta-heuristic Algorithms Throughmentioning

confidence: 99%

Improved Salp Swarm Algorithm Based on Levy Flight and Sine Cosine Operator

Zhang

Wang

2020

IEEE Access

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The salp swarm algorithm (SSA) is a swarm intelligence optimization algorithm that simulates the chain movement behavior of salp populations in the sea. Aiming at the shortcomings of the SSA, such as low precision, low optimization dimension and slow convergence speed, an improved salp swarm algorithm based on Levy flight and sine cosine operator (LSC-SSA) was proposed. The Levy flight mechanism uses the route of short walks combined with long jumps to search the solution space, which can effectively improve the global exploration capability of the algorithm. Improved sine cosine operator use sine search for global exploration and cosine search for local exploitation. At the same time, an adaptively switching between the two function search methods can achieve a smooth transition between global exploration and local exploitation. In the simulation experiment, salp swarm algorithm (SSA), whale optimization algorithm (WOA), particle swarm algorithm (PSO), sine cosine algorithm (SCA), firefly algorithm (FA) and LSC-SSA were adopted for solving function optimization problems. Then, the feasibility of the improved algorithm for solving high-dimensional large-scale optimization problems and the effectiveness of the improvement strategy are evaluated. Finally, LSC-SSA was applied to train muti-layer perceptron neural network. Simulation results show that the introduction of Levy flight and improved sine cosine operator in LSC-SSA significantly improves optimization accuracy and convergence speed compared with other swarm optimization algorithms. In addition, the improved algorithm can effectively solve high-dimensional largescale optimization problems. In the application of training muti-layer perceptron NN, the improved algorithm can avoid falling into the local optimal value and obtain the ideal classification accuracy.

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Parasitism – Predation algorithm (PPA): A novel approach for feature selection

Cited by 58 publications

References 43 publications

An Improved Neural Network Based on Parasitism – Predation Algorithm for an Automatic Voltage Regulator

An Improved Neural Network Based on Parasitism – Predation Algorithm for an Automatic Voltage Regulator

Estimating the optimal parameters of solid oxide fuel cell‐based circuit using parasitism‐predation algorithm

Improved Salp Swarm Algorithm Based on Levy Flight and Sine Cosine Operator

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