Salp Swarm Algorithm: Theory, Literature Review, and Application in Extreme Learning Machines

Faris, Hossam; Mirjalili, Seyedali; Aljarah, Ibrahim; Mafarja, Majdi; Heidari, Ali Asghar

doi:10.1007/978-3-030-12127-3_11

Cited by 106 publications

(56 citation statements)

References 60 publications

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“…T is the weight vector connecting the neuron in the i-th hidden layer with the output neuron [21]. When the quantity of neurons in the hidden layer is equal to that of samples in the training set, for randomly selected ω and b, the SLFNs with N hidden neurons and activation function g(x) can approximate the training samples with zero error, i.e., N i=1 o j − t j = 0 .…”

Section: Extreme Learning Machine and Salp Swarm Algorithm A Extmentioning

confidence: 99%

“…where x 1 j stands for the position of the leader, which changes only according to the position of food source, F j is the position vector of food source in the j-th dimension, ub j and lb j are the upper and lower bounds of the j-th dimension respectively, c 2 and c 3 are random values in the interval [0,1], which are related to whether the leader's next position in the j-th dimension should be positive infinity or negative infinity and the step size. c 1 is the main parameter for balancing search and development in SSA, and its expression is [21]:…”

Section: B Salp Swarm Algorithmmentioning

confidence: 99%

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Ultra-Short-Term Wind Power Prediction by Salp Swarm Algorithm-Based Optimizing Extreme Learning Machine

2020

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Wind power generation accounts for an increasing proportion of the power grid, so efficient and accurate real-time wind power prediction is particularly important for wind power grid. In view of the strong randomness and fluctuation of wind and the difficulty of predicting wind power, a Salp Swarm Algorithms-Extremely Learning Machine (SSA-ELM) based ultra-short-term wind power prediction model is proposed. In this case, the multi-input sample set is composed of historical wind speed, temperature, wind direction, atmospheric pressure and other climatic factors that are highly correlated with wind power, and the network parameters are determined in the training process. In order to improve the adaptability and accuracy of the prediction model, the input weight matrix and hidden layer deviation of the Extreme Learning Machine (ELM) are optimized by exploring and developing the Salp Swarm Algorithm in the iterative process. Finally, the simulation experiment is conducted with the actual data of a wind farm in Henan Province, and the comparison with the traditional Extreme Learning Machine, Particle Swarm Optimization Extreme Learning Machine (PSO-ELM) and Back Propagation (BP) neural network model shows that the new method avoids falling into the local extreme value, and has faster convergence speed and higher prediction accuracy. INDEX TERMS Wind power prediction, salp swarm algorithms, extreme learning machine, optimization algorithm.

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Section: Extreme Learning Machine and Salp Swarm Algorithm A Extmentioning

confidence: 99%

Section: B Salp Swarm Algorithmmentioning

confidence: 99%

Ultra-Short-Term Wind Power Prediction by Salp Swarm Algorithm-Based Optimizing Extreme Learning Machine

2020

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“…SSA is logically demonstrated for the usage in the selection of parameters for HHOA. In this work, HHOA parameters were optimized using salp swarm optimization (SSA) 33 that improves the convergence rate and minimizes the complexity, which is necessary to minimize computational time.…”

Section: Our Proposed Hybrid Hh‐ss Optimization Algorithmmentioning

confidence: 99%

EE‐hHHSS: Energy‐efficient wireless sensor network with mobile sink strategy using hybrid Harris hawk‐salp swarm optimization algorithm

Srinivas

Amgoth

2020

Int J Communication

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Wireless sensor network (WSN) includes power-efficient sensor nodes to convey information to the base station (BS). This network comprises a number of sensor nodes that perform sensing, processing, and wireless communication abilities to monitor a specified sensing field. Therefore, it is necessary to prolong WSN lifetime using energy efficient optimization methods, because the sensor nodes are operated by battery. Also, it is difficult to replace the battery of the nodes located in harsh environments. Thus, energy-efficient routing is a crucial phenomenon in WSN. In this article, energy-efficient WSN with mobile sink (MS) strategy is proposed using hybrid Harris hawk and salp swarm (Hybrid HH-SS) optimization algorithm. In order to achieve energy efficiency, finding an optimal route for MS is a critical task. The MS discovers an optimal path to interconnect with the cluster heads (CHs) by adaptive ant colony optimization (AACO) algorithm. Hence, the proposed hybrid algorithm minimizes the energy consumption (EC), packet loss rate (PLR), and end-to-end (E2E) delay and enhances the lifetime of the network. The proposed work is implemented in JAVA platform, and simulation outcomes show that the proposed approach enhances the wireless sensor network performances. Simulation results show enhancement in energy efficiency in terms of network lifetime, packet delivery rate, average throughput, packet loss rate, energy efficiency, and end-to-end delay. K E Y W O R D S cluster heads (CHs), energy consumption, lifetime, mobile sink, WSN 1 | INTRODUCTION Recently, a well-organized design of WSN has turned into the main zone of research. 1,2 The main features of WSN are environment friendly, appropriate deployment, and self-organizing. For this reason, WSNs are widely useful in fields such as smart health care, environment detection, smart home, and industrial manufacturing. As the sensor nodes are powered using energy-limited batteries, it seems complicated to replace the batteries due to its high cost and a large number of sensors. Hence, it is necessary to adopt an energy efficient WSN with a mobile sink. A device used to identify some sort of input from natural or else physical conditions is called a sensor. The outcome of the sensor is an electrical sign that is transmitted to a controller for further process.

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“…Recently, several metaheuristic algorithms have been devised to solve different optimization problems for instance, Salp Swarm Algorithm [6], Grasshopper Optimization Algorithm [7], Polar Bear Algorithm (Dawid Połap 2017) [8], Coyote Optimization ( Juliano Pierezan and Leandro dos Santos Coelho 2018) [9], and Two Cored Flower Pollination Algorithm (FPA) (Raza 2020) [10]. Devised hybrid modified FPA-Mix-integer linear programming (MILP) algorithm has been compared with these latest algorithms in terms of energy consumption costs, GHG emissions, and executions time for the devised energy management control in a residential µG.…”

Section: Introductionmentioning

confidence: 99%

Risk-Averse Home Energy Management System

Ali

Malik

Raza³

2020

IEEE Access

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Transformation of conventional energy systems into smart grids enables the integration of residential buildings with distributed generations, electro-thermal storages and demand response policies. Further, it improves the household comfort level and helps to preserve the ecological system. Keeping in view the techno-financial impact of residential energy management, optimal handling of residential customers may prove meaningful for peak load reduction, valley filling, and energy conservation. In this regard, this paper devises a residential energy management system (EMS) to optimally schedule appliances, energy sources and electro-thermal storage for reduction of consumption cost and greenhouse (GHG) emissions. Building integrates national grid, natural gas network and solar energy as input carriers, whereas, electricity, heat and cooling as output carriers. To resolve the risk of loss of load, conditional value at risk (CVaR) has been incorporated in the objective function. Comparison demonstrates that under risk-averse approach, energy retaining capability of electric vehicle and thermal energy storage increases by 28.56% and 53.34%, respectively. This stored energy acts as a reserve during absence of solar irradiance and outages on electric and natural gas networks. Moreover, to make EMS more efficient in tracking optimum solutions with faster convergence speed, a hybrid algorithm has been devised by concatenating the modified flower pollination algorithm with mixed-integer linear programming. The proposed algorithm has been validated by comparing its results with the Salp Swarm Algorithm, Grasshopper Optimization Algorithm, Polar Bear Algorithm, Coyote Optimization and Two Cored Flower Pollination Algorithm (FPA). Results manifest that the cost, GHG emissions and execution time drop by 8.98%, 10.81% and 35.064%, respectively. INDEX TERMS Demand response, energy management, residential buildings, smart grids, stochastic model.

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Salp Swarm Algorithm: Theory, Literature Review, and Application in Extreme Learning Machines

Cited by 106 publications

References 60 publications

Ultra-Short-Term Wind Power Prediction by Salp Swarm Algorithm-Based Optimizing Extreme Learning Machine

Ultra-Short-Term Wind Power Prediction by Salp Swarm Algorithm-Based Optimizing Extreme Learning Machine

EE‐hHHSS: Energy‐efficient wireless sensor network with mobile sink strategy using hybrid Harris hawk‐salp swarm optimization algorithm

Risk-Averse Home Energy Management System

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