concept

Binary Whale Optimization Algorithm

Also known as: BWOA

Facts (35)

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Comprehensive framework for smart residential demand side ... nature.com Nature Mar 22, 2025 35 facts

claimThe Binary Whale Optimization Algorithm (BWOA) is proposed as an efficient algorithm for scheduling electric vehicle energy utilization within residential demand side management.

procedureThe authors propose a Binary Whale Optimization Algorithm (BWOA) as an efficient method for scheduling electric vehicle energy utilization within residential demand side management.

procedureThe Binary Whale Optimization Algorithm (BWOA) employs an exploration phase where whales explore the search space randomly to identify potential solutions, and an exploitation phase that uses spiral updating to refine the best solutions.

referenceThe research paper 'New binary Whale optimization algorithm for discrete optimization problems' was published in Eng. Optim. 52 (6), 945–959 in 2020.

imageFigure 6 illustrates the V-shaped transfer function used in the study for the Binary Whale Optimization Algorithm.

claimThe Sparrow Search Algorithm (SSA) and Binary Whale Optimization Algorithm (BWOA) are optimization techniques applied in Residential Energy Management Systems (REMS) to efficiently schedule appliances and reduce electricity costs.

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.

procedureThe Binary Whale Optimization Algorithm restricts position updates to binary values of either zero or one.

procedureThe Binary Whale Optimization Algorithm (BWOA) evaluates fitness using a cost function that incorporates energy consumption and utility costs to assess each whale's performance in achieving energy optimization.

claimThe Binary Whale Optimization Algorithm (BWOA) is designed to optimize Residential Energy Management Systems (REMs), though adjustments to its parameters may be required to address specific problem requirements.

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).

claimThe Binary Whale Optimization Algorithm performs position updates based on the probability of distance, utilizing a transfer function (specifically a sigmoid function) to map distance values to probability values.

claimThe Binary Whale Optimization Algorithm (BWOA) is presented as an approach for the optimal scheduling of energy utilization in residential demand-side management (RDSM) to improve solution-searching capability and control diversity during search stages.

claimThe abbreviation 'BWOA' stands for Binary whale optimization algorithm.

procedureThe Binary Whale Optimization Algorithm uses a sigmoid transfer function to map distance values to probability values for updating agent positions.

referenceThe Binary Whale Optimization Algorithm (BWOA) for Residential Energy Management Systems (REMs) utilizes a population size of 50 whales, an exploration phase for random search, an exploitation phase using spiral updating, an encircling prey mechanism for position adjustment, a convergence mechanism to reduce exploration over time, and a cost function evaluating energy consumption and utility costs.

claimThe Binary Whale Optimization Algorithm restricts position updates to binary values of either zero or one.

measurementThe Binary Whale Optimization Algorithm (BWOA) uses a population size of 50 whales to represent the total number of individuals participating in the optimization process.

procedureThe Binary Whale Optimization Algorithm (BWOA) uses a convergence mechanism that gradually decreases the exploration capability of the whales to ensure convergence towards the optimal solution as the algorithm progresses.

claimThe Binary Whale Optimization Algorithm (BWOA) is proposed as an efficient algorithm for scheduling energy utilization in residential demand side management, specifically considering the impact of electric vehicles.

claimThe Binary Whale Optimization Algorithm (BWOA) is designed to optimize Residential Energy Management Systems (REMs).

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).

procedureThe Binary Whale Optimization Algorithm restricts position updates to binary values of zero or one, utilizing a sigmoid transfer function to map distance values to probability values for position updates.

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.

procedureThe Binary Whale Optimization Algorithm (BWOA) for Residential Energy Management Systems (REMs) utilizes a population size of 50 whales, an exploration phase for random search, an exploitation phase using spiral updating, a mechanism for encircling prey based on the best solution, a convergence mechanism that decreases exploration over time, and a fitness evaluation based on a cost function considering energy consumption and utility costs.

referenceThe discrete/binary version of the Whale Optimization Algorithm (WOA) uses the variable d_i^k(t) to denote the distance of a particle, while x_i^k(t) and x_i^k(t+1) denote the current position of the ith particle at specific iterations and dimensions.

formulaThe discrete/binary version of the Whale Optimization Algorithm (WOA) defines the distance of a particle as d_i^k(t), while x_i^k(t) and x_i^k(t+1) represent the current position of the ith particle at specific iterations and dimensions.

procedureThe Binary Whale Optimization Algorithm (BWOA) mimics the natural behavior of encircling prey by having whales dynamically adjust their positions based on the best solution found so far to converge towards optimal solutions.

claimThe proposed BWOA (Binary Whale Optimization Algorithm) approach is used for achieving optimal scheduling within Residential Demand Side Management (RDSM).

claimThe Binary Whale Optimization Algorithm (BWOA) is designed to optimize Residential Energy Management Systems (REMs) by utilizing specific parameters that may require adjustment based on the unique requirements of the energy scheduling problem.