A particle swarm inspired cuckoo search algorithm for real parameter optimization

Abstract

The cuckoo search algorithm (CS) is a simple and effective global optimization algorithm. It has been successfully applied to solve a wide range of real-world optimization problems. In this paper, inspired by the particle swarm optimization (PSO), the proposed algorithm uses the best individuals among the entire population to enhance the convergence rate of the standard cuckoo search algorithm. While the PSO directly uses the global best solution of the population to determine new positions for the particles at the each iteration, agents of the CS do not directly use this information but the global best solution in the CS is stored at the each iteration. The global best solutions are used to add into the Information flow between the nest helps increase global and local search abilities of the new approach. Therefore, in the first component, the neighborhood information is added into the new population to enhance the diversity of the algorithm. In the second component, two new search strategies are used to balance the exploitation and exploration of the algorithm through a random probability rule. In other aspect, our algorithm has a very simple structure and thus is easy to implement. To verify the performance of PSCS, 30 benchmark functions chosen from literature are employed. The results show that the proposed PSCS algorithm clearly outperforms the basic CS and PSO algorithm. Compared with some evolution algorithms (CLPSO, CMA-ES, GL-25, DE, OXDE, ABC, GOABC, FA, FPA, CoDE, BA, BSA, BDS and SDS) from literature, experimental results indicate that Communicated by V. Loia.the proposed algorithm performs better than, or at least comparable to state-of-the-art approaches from literature when considering the quality of the solution obtained. In the last part, experiments have been conducted on two real-world optimization problems including the spread spectrum radar poly-phase code design problem and the chaotic system. Simulation results demonstrate that the proposed algorithm is very effective.

Personal Best Cuckoo Search Algorithm for Global Optimization

“…Rank population and find the best solution Despite wider range of applications, CS algorithm still needs improvement in search strategy as it suffers from imbalanced exploration and exploitation when the problem becomes complex [19]. This also causes unstable convergence.…”

“…Apart from the applications of CS mentioned above, various modifications and hybrids of the algorithm are proposed in the literature. [13] proposed a PSO-inspired modification in CS to enhance its convergence rate. The modification is made in two components of CS: firstly, to enhance diversity in population, a new population was injected with neighborhood information; secondly, two new search strategies were introduced in CS to balance exploration and exploitation.…”

A Hybrid Seasonal Mechanism with a Chaotic Cuckoo Search Algorithm with a Support Vector Regression Model for Electric Load Forecasting

“…However, as mentioned in [37], the original CS algorithm has some inherent limitations, such as its initialization settings of the host nest location, Lévy flight parameter, and boundary handling problem. In addition, because it is a population-based optimization algorithm, the original CS algorithm also suffers from slow convergence rate in the later searching period, homogeneous searching behaviors (low diversity of population), and a premature convergence tendency [33,38,39].…”

A hybrid method based on krill herd and quantum-behaved particle swarm optimization

“…In order to cope with this limitation, a variety of modern nature-inspired intelligent algorithms has been put forward and applied to solve optimization problems. Some of them, such as particle swarm optimization (PSO) [1][2][3][4], ant colony optimization (ACO) [5][6][7], bat algorithm (BA) [8][9][10][11][12], differential evolution (DE) [13][14][15], firefly algorithm (FA) [16][17][18], biogeographybased optimization (BBO) [19][20][21][22][23], cuckoo search (CS) [24][25][26][27][28], artificial bee colony (ABC) [29][30][31], ant lion optimizer (ALO) [32], multi-verse optimizer (MVO) [33], charged system search (CSS) [34][35][36], gravitational search algorithm (GSA) [37][38][39], animal migration optimization (AMO) [40], interior search algorithm (ISA) [41], grey wolf optimizer (GWO) [42,43], harmony search (HS) [44][45]…”