Research Area: Metaheuristic Computing
This manuscript proposes a new metaheuristic optimization algorithm for solving the problems related to constraint optimization. The proposed algorithm is based on a nature-inspired algorithm like a lightning search algorithm and differential evolution algorithm. The proposed algorithm consists of two stages. In the first/exploitation/local stage, the fitness values are calculated by using a lightning search algorithm, the inclusion of weight factor in the lightning search algorithm, and differential evolution algorithm using the same population. In the second/global/exploration stage, the minimum value of fitness for each row population by the implementation of the above three algorithms is updated with the population. The first and second stage continues until certain criteria are achieved. The final minimum value is the optimized result. Validation of the proposed methodology is performed by using 60 benchmark functions. The proposed methodology is compared with seven other well-known algorithms namely the lightning search algorithm, differential evolution algorithm cuckoo search algorithm, particle swarm optimization algorithm, seagull optimization algorithm, chimp optimization algorithm, and mayfly algorithm by computing mean, standard deviation, best and worst value for 60 benchmark functions. The proposed methodology is also validated by implementing it in seven real-time constraint optimized problems. Furthermore, the efficiency of the proposed technique has been validated statistically for unimodal and multimodal test functions. The results show a better performance of the proposed optimized algorithm compared to other algorithms.
Keywords:
metaheuristic optimization
nature-inspired algorithm
exploitation
local stage
lightning search
unimodal
multimodal
Author(s) Name: Damodar Panigrahy, Padarbinda Samal
Journal name: Engineering Applications of Artificial Intelligence
Conferrence name:
Publisher name: Elsevier
DOI: 10.1016/j.engappai.2021.104419
Volume Information: Volume 105
