Benchmark Functions Optimization Using Binary Biogeography-Based Optimization with Aleatory-Mixed Migration (BBBO-AMM) and Binary Ant-Lion Optimizer (BALO)
Abstract
Problem statement: Metaheuristic optimization algorithms have been taking more impulse in order to improve processes and solve complex problems that require a high computing capacity. These complex problems can have binary terms as variable decisions. There is steel a need for transforming the traditional heuristic algorithms in tools able to handle binary variables.
Current tools: In 2008, the biogeography-based optimization (BBO) algorithm was presented for the first time. This algorithm produced good results by using a model of species migration within ecosystems in order to find the optimal points of benchmark functions. Similarly, ALO, a new optimizer based on the hunting of ant-lion, was released in 2015. These algorithms can handle very well the benchmark functions when the variables are continuous.
Proposal: In this paper, we present a modification to both types of algorithms (BBO and ALO) that will improve the manner how the optimal points are found within the search space. The main modification to both algorithms allows solving problems, in their target functions, with binary decision variables.
Main contributions for each algorithm: An important modification to the first algorithm is how species migrate between ecosystems; this model is based on a modification to the proposal made in 2010. By adding two important features, migration processes are randomly chosen, and a new method for species migration is developed. The manner how species migrate thus becomes random between two migration models. The new proposal for the ALO (second algorithm) solves optimization problems through two different binary random models within the search space.
Validation: To evaluate the behavior of algorithms, fifteen benchmarking functions are used. In addition, a comparison with other optimization algorithms, such as the Binary Particle Swarm Optimization and Gravitational Search Algorithm (BPSOGSA), Genetic Algorithms (GA), and the Binary Bat Algorithm (BBA), is made. We also demonstrate the proposed algorithms for a real-world binary optimization problem.
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