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Grey wolf optimizer (GWO) is an efficient optimization approach in the generation of swarm intelligence based techniques. GWO algorithm relies on the leadership quality and hunting mechanism shown by grey wolves. Half of the iteration in GWO are dedicated to exploration and the rest half are used for exploitation. This article presents a modified GWO approach, known as Howling mechanism based grey...
Spider monkey optimization (SMO) algorithm, is a neoteric Swarm Intelligence based algorithm, inducemented from the social comportment of spider monkeys, commonly known as fission-fusion social system. Despite of perpetuating an equilibrium state betwixt intensification and diversification by its own, SMO sometimes converges at a particular point due to its swarming nature. To overcome this problem,...
Shuffled Frog-Leaping Algorithm (SFLA) is a memetic meta-heuristic approach for solving complex optimization problems. Like other evolutionary algorithms, it may also suffer from the problem of slow convergence. To elevate the convergence and to improve the intensification and diversification capabilities of SFLA, elitism is embedded by calculating the mean of local best and second local best solutions...
Biogeography Based Optimization (BBO) Algorithm is a stochastic and population-based evolutionary search technique modeled on the theory of biogeography. Like other population-based evolutionary algorithms, BBO also suffers from the problem of slow convergence. Therefore, in this article, a new variant of BBO algorithm is introduced, namely Fast Convergent Biogeography based Optimization (FCBBO) Algorithm...
Biogeography Based Optimization (BBO) algorithm is a population based evolutionary optimization algorithm modeled on the theory of biogeography. Like other evolutionary algorithms, BBO also suffers from the problem of slow convergence. To improve the convergence property of the algorithm, global best solution inspired search strategy is incorporated with BBO. The modified strategy is named as global-best...
Shuffled Frog-Leaping Algorithm (SFLA) is a memetic meta-heuristic used for solving various combinatorial optimization problems. SFLA divides population into several memeplexes and then apply evolutionary process to update every memeplex. Like other evolutionary algorithm, it may also suffer from the problem of slow convergence. To improve the convergence and exploitation capability of SFLA, Binomial...
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