Solution of structural and mathematical optimization problems using a new hybrid swarm intelligence optimization algorithm

•A new hybrid meta-heuristic optimization algorithm named as ISA (Interactive Search algorithm) is developed.•ISA combines integrated particle swarm optimization and teaching learning based optimization.•The novel algorithm (ISA) considers interaction with other particles besides the global and prev...

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Vydáno v:	Advances in engineering software (1992) Ročník 127; s. 106 - 123
Hlavní autoři:	Mortazavi, Ali, Toğan, Vedat, Moloodpoor, Mahsa
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.01.2019
Témata:	Hybrid optimization method Integrated particle swarm optimization (iPSO) Teaching and learning based optimization (TLBO) Teaching and learning based optimization (TLBO) Integrated particle swarm optimization (iPSO) Hybrid optimization method
ISSN:	0965-9978
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Popis
Shrnutí:	•A new hybrid meta-heuristic optimization algorithm named as ISA (Interactive Search algorithm) is developed.•ISA combines integrated particle swarm optimization and teaching learning based optimization.•The novel algorithm (ISA) considers interaction with other particles besides the global and previous best particles to enhance the search ability. In this investigation a new optimization algorithm named as interactive search algorithm (ISA) is presented. This method is developed through modifying and hybridizing the affirmative features of recently developed integrated particle swarm optimization (iPSO) algorithm with the pairwise knowledge sharing mechanism of the teaching and learning based optimization (TLBO) method. Proposed ISA provides two different navigation schemes as Tracking and Interacting. Each agent based on its tendency factor can pick one of these two schemes for searching the domain. Additionally, ISA utilizes an improved fly-back technique to handle problem constraints. The proposed method is tested on a set of mathematical and structural optimization benchmark problems with discrete and continuous variables. Numerical results indicate that the new algorithm is competitive with other well-stablished metaheuristic algorithms.
ISSN:	0965-9978
DOI:	10.1016/j.advengsoft.2018.11.004