Abstract
Integrating heterogeneous biological-inspired strategies and mechanisms into one algorithm can avoid the shortcomings of single algorithm. This article proposes an integrated cuckoo search optimizer (ICSO) for single objective optimization problems, which incorporates the multiple strategies into the cuckoo search (CS) algorithm. The paper also considers the proposal of multi-objective versions of ICSO called MOICSO. The two algorithms presented in this paper are benchmarked by a set of benchmark functions. The comprehensive analysis of the experimental results based on the considered test problems and comparisons with other recent methods illustrate the effectiveness of the proposed integrated mechanism of different search strategies and demonstrate the performance superiority of the proposed algorithm.
Highlights
Meta-heuristic algorithm provides a wide space for solving optimization problems from a new perspective
In order to solve all kinds of optimization problems, many novel meta-heuristics algorithms are developed, such as Ant Colony Optimization (ACO) (Dorigo & Gambardella, 1997), Simulated Annealing (SA) (Kirkpatrick, Gelatt & Vecchi, 1983), Genetic Algorithms (GA) (Holland, 1975), Taboo Search (TS) (Al-Sultan & Fedjki, 1997), Particle Swarm Optimization (PSO) (Kennedy & Eberhart, 1995), Artificial Bee Colony (ABC) (Karaboga & Basturk, 2007), Firefly Algorithm (FA) (Yang, 2010), Cuckoo Search (CS) (Yang, 2014), Pathfinder Algorithm (PFA) (Yapici & Cetinkaya, 2019), Dragonfly Algorithm (DA) (Mirjalili, 2016) and Water Cycle Algorithm (WCA) (Sadollah et al, 2015)
The high fitness subpopulation is responsible for exploiting better solutions, while the low fitness subpopulation is responsible for exploring unknown solutions
Summary
Meta-heuristic algorithm provides a wide space for solving optimization problems from a new perspective. This strategy may deteriorate the convergence speed and the quality of solution due to interference phenomena among dimensions when solving multi-dimension function optimization problems To overcome this shortage, a dimension by dimension improvement based. To achieve good optimization performance with higher convergence speed and avoid falling into local optimal solutions, an ICSO is proposed by introducing multiple strategies into the original CS algorithm. Based on the above two points, in order to make step size adapt to different optimization problems, a self-adaptive step size mechanism for high fitness subpopulation is introduced. Crossover based on DE operation Because the two subpopulations adopt different search strategies, the solutions have diversity on the whole, seen from Fig. 2.
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