concept

Slime Mould Algorithm

Also known as: SMA

Facts (14)

Sources

Demand side management using optimization strategies for efficient ... journals.plos.org PLOS ONE Mar 21, 2024 14 facts

referenceThe Slime Mould Algorithm (SMA) is an optimization method inspired by the slime mould's ability to find the shortest path to food sources, as cited in reference [30].

referenceThe Slime mould algorithm is a stochastic optimization method introduced by A. and Mirjalili S. in the paper 'Slime mould algorithm: A new method for stochastic optimization,' published in Future Generation Computer Systems, volume 111, pages 300–323, in October 2020.

claimThe Slime Mould Algorithm mimics the natural oscillation pattern of slime mould, which dynamically adjusts its body shape to optimize nutrient intake while minimizing exposure to threats.

codeAlgorithm: Slime Mould Algorithm for Demand Side Management Input: Population size N, Maximum iterations Tmax, Search Space Dimensions Output: Slime Mould Pathway for load management pattern 1: Initialize the slime mould population Xi for i = 1 to N 2: Evaluate the fitness of each slime mould 3: Determine the best solution Xbest 4: While (t < Tmax) do 5: for each slime mould i do 6: Update the position of slime mould i towards the best solution 7: Oscillate to explore the search space 8: Evaluate the updated fitness 9: If the new position is better, update the individual and global best 10: end for 11: t = t + 1 12: end while 13: Return the optimized DSM schedule Xbest

procedureThe Slime Mould Algorithm for Demand Side Management proceeds in the following steps: (1) Initialization: Generate an initial population of slime moulds with random positions within the permissible DSM strategy space. (2) Fitness Evaluation: Evaluate the fitness of each slime mould based on the objective function (Dpeak). (3) Loop Until Convergence: For each iteration, update positions based on fitness, simulate rhythmic contraction and expansion (oscillation phase), reinforce strategies yielding lower peak loads (positive feedback), adjust search strategy based on stochastic oscillation (adaptation), and retain the best-found solutions (selection). (4) Output the Best Solution: Display the best-found solution upon reaching the final iteration.

claimThe CHHO algorithm shows a better convergence rate compared to the SMA and other algorithms, performing well for objective functions and constraints when considering all loads in the GN system.

claimThe Slime Mould Algorithm (SMA) for Demand Side Management (DSM) balances exploration and exploitation by diversifying the search for solutions and intensifying the search around promising regions, similar to how slime mould strengthens beneficial paths in nature.

claimV. MK, Chokkalingam B, and S. D. utilized several optimization strategies to construct objective functions for their Demand Side Management algorithm, including the Bat Optimization Algorithm (BOA), African Vulture Optimization (AVOA), Cuckoo Search Algorithm, Chaotic Harris Hawk Optimization (CHHO), Chaotic-based Interactive Autodidact School (CIAS) algorithm, and Slime Mould Algorithm (SMA).

procedureThe optimization algorithms (BOA, AVOA, CS, CHHO, CIAS, and SMA) were evaluated on a high-performance Intel i7 13th gen 1335 processor with 16 GB RAM to determine the best method in terms of result and speed.

claimThe CHHO (Harris Hawks Optimization) algorithm performs better than other algorithms in residential loads, while the SMA (Slime Mould Algorithm) performs better alongside CHHO in the IT sector load.

measurementThe Slime Mould Algorithm (SMA) for Demand Side Management achieves a peak load of 3.4 MW in the residential sector and 3.1 MW in the IT sector.

claimIn the Slime Mould Algorithm, the 'Oscillation Pattern' behavior models the rhythmic shape changes of slime mould to search through the solution space.

referenceThe study utilizes several optimization algorithms to construct objective functions for Demand Side Management (DSM), including the Bat Optimization Algorithm (BOA), African Vulture Optimization Algorithm (AVOA), Adaptive Neuro-Fuzzy Inference System (ANFIS), Cuckoo Search (CS) Algorithm, Chaotic Harris Hawk Optimization (CHHO), Chaotic-based Interactive Autodidact School (CIAS) algorithm, and the Slime Mould Algorithm (SMA).

claimThe Slime Mould Algorithm (SMA) is distinguished for its ability to avoid local optima in global optimization challenges for demand-side management.