Journal of Global Optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
GSA: A Gravitational Search Algorithm
Information Sciences: an International Journal
Nature-Inspired Metaheuristic Algorithms
Nature-Inspired Metaheuristic Algorithms
The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm
International Journal of Bio-Inspired Computation
Imperialist competitive algorithm for minimum bit error rate beamforming
International Journal of Bio-Inspired Computation
Firefly algorithms for multimodal optimization
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
New inspirations in swarm intelligence: a survey
International Journal of Bio-Inspired Computation
Two-stage eagle strategy with differential evolution
International Journal of Bio-Inspired Computation
International Journal of Bio-Inspired Computation
Test sequence optimisation: an intelligent approach via cuckoo search
International Journal of Bio-Inspired Computation
Optimal design of constraint engineering systems: application of mutable smart bee algorithm
International Journal of Bio-Inspired Computation
Multi-agent simulated annealing algorithm based on differential evolution algorithm
International Journal of Bio-Inspired Computation
The great salmon run: a novel bio-inspired algorithm for artificial system design and optimisation
International Journal of Bio-Inspired Computation
International Journal of Bio-Inspired Computation
International Journal of Bio-Inspired Computation
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This paper aims to evaluate the ability of some well-known bio-inspired metaheuristics for optimal arrangement of thermoelectric cells mounted in a thermal component. In real life applications, proper arrangement of thermoelectric modules plays a pivotal role by maximising the generated electricity. However, some defects such as the increase in total maintenance cost is often associated with the use of thermoelectric cells. Hence, it is mandatory to contrive a policy which guarantees the maximum electricity generation while keeps the maintenance cost in lowest level. Here, authors use both adaptive neuro-fuzzy inference system ANFIS and experimental data to model the power generation and maintenance cost of thermoelectric cells. At the next step, they engage some famous bio-inspired metaheuristic algorithms, i.e., bee algorithm BA, particle swarm optimisation PSO and the great salmon run TGSR to arrange the thermoelectric cells in a cost effective manner. The gained results indicate that the proposed algorithms are highly capable to find an efficient arrangement for thermoelectric cells within a rational duration. Besides, through independent runs, it is observed that metaheuristics show acceptable robustness for the current case study.