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Salp Swarm Algorithm (SSA)
Also known as: SSA, Salp Swarm Algorithm (SSA), Salp Swarm Algorithm
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Comprehensive framework for smart residential demand side ... nature.com Mar 22, 2025 63 facts
procedureIn the Salp Swarm Algorithm (SSA), the population of solution vectors is divided into a leader salp, positioned at the front of the chain, and follower salps, whose movement is influenced by both the leader and their peers to converge towards optimal solutions.
procedureThe Salp Swarm Algorithm (SSA) for Residential Energy Management Systems (REMs) utilizes a population size of 50 salps, movement governed by Levy flight, leader selection based on fitness and proximity to food sources, an adaptation rate of 0.1, and a fitness evaluation based on a cost function considering energy consumption and utility costs.
formulaIn the Salp Swarm Algorithm, the position of the ith follower salp in the jth dimension is calculated using the formula x_j^i = (x_j^i + x_j^{i-1}) / 2, where i >= 2, x_j^i is the new position of the ith follower, and x_j^{i-1} is the position of the previous salp in the chain.
claimThe Salp Swarm Algorithm (SSA) organizes salps into a chain divided into two phases: the leader phase and the followers' phase.
claimIn the Salp Swarm Algorithm (SSA), the follower phase updates the positions of follower salps based on their proximity to both the leader and the preceding follower in the chain, ensuring coordinated movement and structural integrity.
measurementIn Scenario 2 (With REM, no RES), the Beluga Whale Optimization Algorithm (BWOA) achieves 16.26% electricity cost savings compared to the Salp Swarm Algorithm's (SSA) 13.56% savings.
claimThe Salp Swarm Algorithm (SSA) is a meta-heuristic optimization technique inspired by the natural navigating and foraging behavior of deep-sea salps.
procedureThe Salp Swarm Algorithm (SSA) identifies leaders based on their fitness and proximity to the food source to ensure the swarm is guided by individuals closest to the optimal solution.
claimThe parameter c1 in the Salp Swarm Algorithm is a random number within [0,1] that significantly impacts the algorithm's optimal searching capability during both exploration and exploitation stages.
claimThe abbreviation 'SSA' stands for Salp swarm algorithm.
claimIn the Salp Swarm Algorithm, follower salps move based on two factors: following the leader toward better solutions and being influenced by their peers.
claimIn the Salp Swarm Algorithm, the leader salp is positioned at the front of the chain and steers the swarm toward the food source by dynamically adjusting its position based on the food source location and specific algorithmic parameters.
measurementIn Scenario 3 (With REM and RES), the Beluga Whale Optimization Algorithm (BWOA) achieves 25.29% electricity cost savings compared to the Salp Swarm Algorithm's (SSA) 16.82% savings.
claimThe study follows the Salp Swarm Algorithm (SSA) and the Binary Whale Optimization Algorithm (BWOA) to address optimization issues.
claimProsumers (smart users) utilize Residential Energy Management (REM) architectures, incorporating algorithms like the Salp Swarm Algorithm (SSA) and the Binary Whale Optimization Algorithm (BWOA), to facilitate load shifting and efficient energy scheduling.
claimProsumers (smart users) utilize Residential Energy Management (REM) architectures, such as the Salp Swarm Algorithm (SSA) and the Binary Whale Optimization Algorithm (BWOA), to facilitate load shifting and efficient energy scheduling.
referenceThe research paper 'Improved salp swarm algorithm based on particle swarm optimization for feature selection' was published in J. Ambient Intell. Humaniz. Comput. 10, 3155–3169 in 2019.
measurementIn Scenario 1 (No Renewable Energy Management or Renewable Energy Sources), the Beluga Whale Optimization Algorithm (BWOA) achieved 7.99% electricity cost savings compared to 4.70% savings achieved by the Salp Swarm Algorithm (SSA).
procedureThe Salp Swarm Algorithm follows a specific procedure: (1) formulate a population matrix of solution vectors, (2) initialize variable values within defined limits, (3) apply an objective function to compute fitness values for all solution vectors, and (4) identify the best position (FF) based on fitness to serve as the food source for the swarm.
claimIn the Salp Swarm Algorithm (SSA), the parameter c1 has a greater impact on the optimal searching capability of the process, particularly in both the exploration and exploitation stages, compared to other randomly chosen parameters.
claimThe Salp Swarm Algorithm divides the population of solution vectors into two groups: a leader salp positioned at the front of the chain and follower salps.
formulaIn the Salp Swarm Algorithm, the parameter c1 is dynamically updated at each iteration to balance exploration and exploitation, with the formula c1 = 2 * exp(-(4 * t / T)^2), where t is the current iteration and T is the total number of iterations.
claimThe Salp Swarm Algorithm (SSA) organizes the population into two distinct phases: the leader phase and the followers' phase.
claimIn the Salp Swarm Algorithm (SSA), the leader salp is situated at the front of the chain and steers the swarm toward the food source.
referenceThe Salp Swarm Algorithm (SSA) for Residential Energy Management Systems (REMs) utilizes a population size of 50 salps, a Levy flight movement pattern, a leader selection process based on fitness and proximity to food sources, an adaptation rate of 0.1, and a cost function evaluating energy consumption and utility costs.
measurementIn Scenario 2 (With Renewable Energy Management, no Renewable Energy Sources), the Beluga Whale Optimization Algorithm (BWOA) achieved 16.26% electricity cost savings compared to 13.56% savings achieved by the Salp Swarm Algorithm (SSA).
measurementIn Scenario 1 (No REM or RES), the Beluga Whale Optimization Algorithm (BWOA) achieves 7.99% electricity cost savings compared to the Salp Swarm Algorithm's (SSA) 4.70% savings.
claimThe Beluga Whale Optimization Algorithm (BWOA), inspired by the feeding behavior of whales, outperforms the Salp Swarm Algorithm (SSA) in Home Energy Management Systems by utilizing advanced optimization techniques that enable faster convergence, dynamic adaptation to environmental conditions, and superior performance in minimizing electricity costs and maintaining grid stability.
procedureThe study follows a hybrid approach for Residential Demand Side Management (RDSM) optimization by using the Salp Swarm Algorithm (SSA) followed by the Binary Whale Optimization Algorithm (BWOA).
claimA binary version of the Salp Swarm Algorithm (SSA) is utilized in this study to adapt the algorithm for discrete optimization problems, demonstrating its flexibility in handling binary search spaces.
claimThe Beluga Whale Optimization Algorithm (BWOA) is an optimization method inspired by the feeding behavior of whales that outperforms the Salp Swarm Algorithm (SSA) in Home Energy Management Systems (HEMS) by utilizing advanced techniques for faster convergence and dynamic adjustment to environmental conditions.
referenceFigures 10-14 illustrate Electric Vehicle (EV) integration for Case I, while Figures 15-19, 20-24, and 25-29 depict results for Cases II, III, and IV, respectively, showcasing load patterns, electricity costs, and the performance of smart users employing Salp Swarm Algorithm (SSA) and Black Widow Optimization Algorithm (BWOA).
claimThe Salp Swarm Algorithm (SSA) is an optimization method inspired by the swarming behavior of salps that demonstrates strengths in global exploration and convergence, allowing it to avoid local optima.
claimThe Beluga Whale Optimization Algorithm (BWOA) is an optimization method inspired by the feeding behavior of whales that consistently outperforms the Salp Swarm Algorithm (SSA) in optimizing Home Energy Management Systems (HEMS).
claimThe Salp Swarm Algorithm (SSA) is an optimization method inspired by the swarming behavior of salps that demonstrates strengths in global exploration and convergence, though it may be limited by slower convergence and less refined strategies compared to other algorithms in complex energy management scenarios.
claimThe Salp Swarm Algorithm (SSA) dynamically updates the parameter c1 at each iteration to enhance the algorithm's searching capability, with the value decreasing as the algorithm progresses to transition from exploration to exploitation.
claimThe study utilizes a binary version of the Salp Swarm Algorithm to adapt the algorithm for discrete optimization problems, demonstrating its effectiveness in binary search spaces.
claimIn the Salp Swarm Algorithm, follower salps update their positions based on their proximity to both the leader salp and the preceding follower in the chain, ensuring coordinated movement and structural integrity.
referenceThe research paper 'Salp swarm algorithm: A bio-inspired optimizer for engineering design problems' was published in Adv. Eng. Softw. 114, 163–191 in 2017.
claimThe abbreviation 'SSA' stands for Salp swarm algorithm.
claimThe Salp Swarm Algorithm (SSA) defines the food source position as the optimal or near-optimal solution in the search space, which acts as a guide for all salps to improve their positions.
procedureThe Salp Swarm Algorithm optimization process follows these steps: (1) formulate a population matrix of solution vectors, (2) initialize variable values within defined limits, (3) apply an objective function to compute fitness values for all solution vectors, and (4) identify the best position (FF) based on fitness value to serve as the target food source for the salp chain.
claimThe Salp Swarm Algorithm (SSA) exhibits slower convergence and less refined optimization strategies compared to the Beluga Whale Optimization Algorithm (BWOA), limiting its effectiveness in complex energy management scenarios.
claimThe Salp Swarm Algorithm (SSA) and Black Widow Optimization Algorithm (BWOA) are optimization techniques applied in Residential Energy Management Systems (REMS) to efficiently schedule appliances and reduce electricity costs.
referenceThe Salp Swarm Algorithm (SSA) is a meta-heuristic optimization technique inspired by the natural navigating and foraging behavior of salps in the deep sea, specifically their swarming behavior known as the 'salp chain'.
claimThe study follows the Salp Swarm Algorithm (SSA) and the Binary Whale Optimization Algorithm (BWOA) to address optimization issues in Residential Demand Side Management (RDSM).
measurementThe Salp Swarm Algorithm (SSA) uses an adaptation rate of 0.1, which determines the rate at which salps adjust their positions in response to the best solutions found during the search process.
claimThe Salp Swarm Algorithm (SSA) is a meta-heuristic optimization technique inspired by the natural foraging and swarming behavior of salps in the deep sea, specifically their 'salp chain' movement.
claimThe Salp Swarm Algorithm (SSA), inspired by the swarming behavior of salps, is an optimization method that demonstrates strengths in global exploration and convergence but is less effective than the Beluga Whale Optimization Algorithm (BWOA) in load shifting and electricity cost reduction for Home Energy Management Systems.
procedureIn the Salp Swarm Algorithm, the population of solution vectors is divided into a leader salp, which guides the swarm toward better solutions, and follower salps, which are influenced by both the leader and their peers to converge toward optimal solutions.
claimIn the prosumer scenario (Scenario-II), smart users utilize Residential Energy Management (REM) architectures and algorithms such as the Salp Swarm Algorithm (SSA) and the Binary Whale Optimization Algorithm (BWOA) to shift loads to off-peak hours, resulting in reduced electricity costs and alleviated peak load demands on the grid.
claimIn the Salp Swarm Algorithm, the leader salp is positioned at the front of the chain and steers the swarm toward the food source by dynamically adjusting its position based on the food source location and specific algorithmic parameters.
measurementIn Scenario 3 (With Renewable Energy Management and Renewable Energy Sources), the Beluga Whale Optimization Algorithm (BWOA) achieved 25.29% electricity cost savings compared to 16.82% savings achieved by the Salp Swarm Algorithm (SSA).
claimSalps exhibit a swarming behavior called a "salp chain," which improves their locomotion during foraging and serves as the basis for the Salp Swarm Algorithm's searching capability.
claimThe Salp Swarm Algorithm (SSA) divides the movement of the salp population into two distinct phases: the leader phase and the followers’ phase.
procedureThe Salp Swarm Algorithm (SSA) utilizes Levy flight for movement, which is a random walk characterized by step lengths following a Levy distribution to enable efficient exploration of the search space.
measurementIn Scenario 2 (With Residential Energy Management, no Renewable Energy Sources), the Beluga Whale Optimization Algorithm (BWOA) achieved 16.26% savings compared to the Salp Swarm Algorithm's (SSA) 13.56% savings.
measurementThe Salp Swarm Algorithm (SSA) uses a population size of 50 salps to represent the total number of individuals in the population for residential energy management systems.
claimThe Salp Swarm Algorithm dynamically updates the parameter c1 at each iteration to ensure it gradually decreases, facilitating a transition from exploration in early stages to exploitation in later stages.
procedureThe Salp Swarm Algorithm (SSA) optimization process begins by formulating a population matrix of solution vectors, initializing variable values within defined limits, and applying an objective function to compute fitness values, where the best position (FF) serves as the food source for the swarm.
measurementIn Scenario 1 (No Residential Energy Management or Renewable Energy Sources), the Beluga Whale Optimization Algorithm (BWOA) achieved 7.99% savings compared to the Salp Swarm Algorithm's (SSA) 4.70% savings.
procedureThe Salp Swarm Algorithm (SSA) evaluates fitness using a cost function that considers energy consumption and utility costs to assess each salp's performance in achieving energy optimization.
measurementIn Scenario 3 (With Residential Energy Management and Renewable Energy Sources), the Beluga Whale Optimization Algorithm (BWOA) achieved 25.29% savings compared to the Salp Swarm Algorithm's (SSA) 16.82% savings.