The efficiency analysis of bioinspired global optimization methods

Аuthors

Orlovskaya N. M.

Moscow Aviation Institute (National Research University), 4, Volokolamskoe shosse, Moscow, А-80, GSP-3, 125993, Russia

e-mail: orlovskaya.nataly@yandex.ru

Abstract

An aerospace systems design is based on the optimization problems solving. This design phase is very important, because it allows to find the best combination of characteristic parameter values, that make using of aircraft more effective. For this reason, selection of an efficient optimization method is very important.
This paper considers four bioinspired global optimization methods that are stochastic search optimization technique. Bioinspired methods belong to the group of metaheuristic methods that mimic the natural biological processes and the social behavior of some animal and plant species. These methods become more and more popular, because they allow to find the solution of optimization problems, for which searching optimum solutions with traditional mathematical techniques are ineffective.
Cuckoo search (CS) and Weed Colonization (WC) are evolutionary-based methods. The both methods are inspired by the idea of a new population generation. It means that some of the individuals (possible solutions) with the worst fitness are removed and replaced by the better solutions as a result of natural selection process.
Shuffled Frog Leaping Algorithm (SFLA) and Glowworm Swarm Optimization (GSO) are methods of «swarm» intelligence. The agents of «swarm» intelligence system interact with each other and exchange information in population. Shuffled frog leaping algorithm based on observation of the frogs in population when they search for food.
The main advantage of presented bioinspired methods is solving multiextremal optimization problems with a large number of variables. They do not guarantee convergence to the global optimum, but they allow to obtain a good solution within a reasonable period of time from a practical point of view. These reasons make the applying of bioinspired global optimization methods perspective for solving the complex optimization problems in the aerospace system design process.

Keywords:

optimization, global extremum, population, fitness, objective function, bioinspired methods

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