Modified biogeography based optimization

Several scholars have been working for enhancing the exploration ability.

Biogeography-based optimization

Then, a local search mechanism is used in BBO to supplement with modified migration operator. Introduction In practical application, many problems are regarded as optimization problems. Biogeography-based optimization BBOproposed by Simon [8], is a new entrant in the domain Modified biogeography based optimization global optimization based on the theory of biogeography.

Several effective techniques have been developed for solving optimization problems. Just as species, in biogeography, migrate back and forth between habitats, features in candidate solutions are shared between solutions through migration operator. To mention just a few examples, Gong et al. Extensive experimental tests are conducted on 27 benchmark functions to show the effectiveness of the proposed algorithm.

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. 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.

Finally, the performance of the modified migration operator and local search mechanism are also discussed. However, these features, in the origin good solution, may exist in several solutions, both good and poor solutions, which may weaken exploration ability.

Biogeography-based optimization BBO is a new effective population optimization algorithm based on the biogeography theory with inherently insufficient exploration capability.

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].

In MLBBO, a modified migration operator is integrated into BBO, which can adopt more information from other habitats, to enhance the exploration ability. Good solutions tend to share their features with poor solutions. Compared with traditional techniques, evolutionary algorithms can solve optimization problems without using some information such as differentiability.Biogeography-based optimization (BBO) is a new effective population optimization algorithm based on the biogeography theory with.

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.

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.

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.

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.

Modified Biogeography-Based Optimization with Local Search Mechanism Download
Modified biogeography based optimization
Rated 3/5 based on 59 review