concept

Beluga Whale Optimization Algorithm

Also known as: BWOA

Facts (15)

Sources

Comprehensive framework for smart residential demand side ... nature.com Nature Mar 22, 2025 15 facts

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.

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.

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).

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.

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.

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) 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), 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.

claimThe Beluga Whale Optimization Algorithm (BWOA) utilizes dynamic adjustment mechanisms to adapt to varying environmental conditions, which aids in minimizing electricity costs, achieving smooth load shifting, and maintaining grid stability.

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).

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.

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.

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.