Solving single-objective optimization problems based on the enhanced chimpanzee optimizer algorithm (with Matlab code)
Chimpanzee Optimization Algorithm (COA) is an intelligent optimization algorithm based on bionics, inspired by the behavior of chimpanzees. By simulating the organizational structure and behavioral characteristics of a chimpanzee population, COA can be used to solve various single-objective optimization problems. In this article, we will introduce the Enhanced Chimpanzee Optimization (ECOA) algorithm and provide corresponding Matlab code examples.
Algorithm principle
The enhanced chimpanzee optimizer algorithm (ECOA) is improved on the basis of COA. By introducing enhancement factors and adaptive weights, the search capability and global convergence of the algorithm are improved. The main steps of ECOA are as follows:
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Initialization parameters: including the initial position of the chimpanzee individual, group size, maximum number of iterations, etc.
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Generate initial population: Based on the given initial position, generate an initial chimpanzee population.
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Calculate fitness value: For each chimpanzee individual, calculate its fitness value as an indicator to evaluate individual performance.
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Update enhancement factor: Based on the fitness value, update the enhancement factor, which is used to adjust the search behavior of individual chimpanzees.
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Update weight: Update the adaptive weight according to the fitness value, and the weight is used to adjust the cooperative behavior between individual chimpanzees.
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Update position: Update the position of the individual chimpanzee based on the current position, enhancement factors and adaptive weights.
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Determine the termination condition: check whether the maximum number of iterations is reached or the stopping criterion is met. If so, the algorithm ends; otherwise, return to step 3.
Matlab code example