Abstract
The grasshopper optimization algorithm (GOA) is a new meta-heuristic algorithm inspired by the behavior of grasshopper groups. Aiming at the shortcomings of poor development ability and low convergence accuracy of GOA, this paper introduces the gravity search operator into the optimization process of GOA to improve the grasshopper’s global exploration and avoid falling into local optimum in advance. At the same time, a pigeon search operator-landmark operator is introduced to improve and balance the algorithm’s exploration and development capabilities. In order to verify the validity of the improved algorithm, this paper will adopts the gravity search operator and a deterrent landmark operator hybrid grasshoppers algorithm (HGOA) with basic grasshopper algorithm (GOA), particle swarm optimization (PSO) algorithm, sine and cosine algorithm (SCA), moth-flame optimization (MFO) algorithm, salp swarm algorithm (SSA), and bat algorithm (BA) to optimize 28 test functions. And the analysis and comparison of the obtained statistical data results finally show that the proposed improved grasshopper algorithm has better optimization ability.
Highlights
Optimization is the process of finding the best solution for a particular problem
In order to verify the performance of the improved algorithm, this paper compares the improved hybrid grasshopper algorithm (HGOA) with the original grasshopper algorithm (GOA), particle swarm optimization (PSO) algorithm, sine cosine algorithm (SCA), moth flame optimization (MFO) algorithm, salp swarm algorithm (SSA) and bat algorithm (BA)
Because there are a large number of local solutions in the multi-modal test function, these results quantitatively show the effectiveness of the algorithm to avoid local solutions in the optimization process
Summary
Optimization is the process of finding the best solution for a particular problem. As the complexity of the problem increases, the need for new optimization techniques has become more apparent in the past few decades than before. Since the algorithm was proposed, it has been widely used in many fields, such as optimal power flow [19], feature selection [20], financial stress prediction [21], and image segmentation [22] This swarm based meta-heuristic algorithm avoids the stagnation of local optimality to some extent, but it is found in the test function that GOA has the disadvantages of poor global search ability and slow convergence speed. This paper introduces the gravity search operator (GGOA) on the basis of the original GOA, which improves the randomness of the algorithm, increases the global search ability of the algorithm, and avoids falling into local optimization On this basis, pigeon landmark operator is added to improve and balance the exploration and exploitation ability of the algorithm. The selection strategy is adopted in the location update strategy, which further increases the selection diversity of the algorithm and improves the convergence accuracy of the algorithm
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