Modified biogeography based optimization

Compared with traditional techniques, evolutionary algorithms can solve optimization problems without using some information such as differentiability. To mention just a few examples, Gong et al. Traditional techniques [1, 2] are effective methods for solving these problems, but they need to know the property of problems, such as continuity or differentiability.

Several scholars have been working for enhancing the exploration ability. Biogeography-based optimization BBO is a new effective population optimization algorithm based on the biogeography theory with inherently insufficient exploration capability.

BBO is developed through simulating the emigration and immigration of species between habitats in the multidimensional solution space, where each habitat represents a candidate solution.

In MLBBO, a modified migration operator is integrated into BBO, which can adopt more information from other habitats, to enhance the exploration ability. Biogeography-based optimization BBOproposed by Simon [8], is a new entrant in the domain of global optimization based on the theory of biogeography.

Introduction In practical application, many problems are regarded as optimization problems. However, these features, in the origin good solution, may exist in several solutions, both good and poor solutions, which may weaken exploration ability.

Extensive experimental tests are conducted on 27 benchmark functions to show the effectiveness of the proposed algorithm. Just as species, in biogeography, migrate back and forth between habitats, features in candidate solutions are shared between solutions through migration operator.

Good solutions tend to share their features with poor solutions.

Modified Biogeography-Based Optimization with Local Search Mechanism

Then, a local search mechanism is used in BBO to supplement with modified migration operator. In the past few decades, various evolutionary algorithms have been sprung up for solving complex optimization problems, for example, genetic algorithm GA [3], evolutionary programming EP [4], particle swarm optimization PSO [5], Ant Colony optimization ACO [6], differential evolution DE [7], and biogeography-based optimization BBO [8].

Several effective techniques have been developed for solving optimization problems. Finally, the performance of the modified migration operator and local search mechanism are also discussed.Biogeography-based optimization (BBO) is a new effective population optimization algorithm based on the biogeography theory with.

Biogeography-based optimization (BBO) is an evolutionary algorithm (EA) that optimizes a function by stochastically and iteratively improving candidate solutions with regard to a given measure of quality, or fitness function. Paper Title Modified Biogeography Based Optimization (MBBO) Authors Komal Mehta, Raju Pal Abstract Biogeography based optimization is most familiar meta-heuristic optimization technique based on.

Biogeography-based optimization

Algorithm (DE) and Biogeography-based Optimization are the most popular and widely used evolutionary algorithm lies in this category [1]. In this paper, we focused on evolutionary based biogeography based optimization (BBO) algorithms.

The solution generated in BBO based algorithm is known as habitat.

Modified Biogeography Based Optimization and enhanced simulated annealing on Travelling Tournament problem. Abstract: This paper shows the implementation of Modified BBO and Extended BBO on Travelling Tournament Problem.

Biogeography based optimization (BBO) has recently gain interest of researchers due to its efficiency and existence of very few parameters.

The BBO is inspired by geographical distribution of species within islands.

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Modified biogeography based optimization
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