An Enhanced Comprehensive Learning Particle Swarm Optimizer with the Elite-Based Dominance Scheme

Abstract

In recent years, swarm-based stochastic optimizers have achieved remarkable results in tackling real-life problems in engineering and data science. When it comes to the particle swarm optimization (PSO), the comprehensive learning PSO (CLPSO) is a well-established evolutionary algorithm that introduces a comprehensive learning strategy (CLS), which effectively boosts the efficacy of the PSO. However, when the single modal function is processed, the convergence speed of the algorithm is too slow to converge quickly to the optimum during optimization. In this paper, the elite-based dominance scheme of another well-established method, grey wolf optimizer (GWO), is introduced into the CLPSO, and the grey wolf local enhanced comprehensive learning PSO algorithm (GCLPSO) is proposed. Thanks to the exploitative trends of the GWO, the algorithm improves the local search capacity of the CLPSO. The new variant is compared with 15 representative and advanced algorithms on IEEE CEC2017 benchmarks. Experimental outcomes have shown that the improved algorithm outperforms other comparison competitors when coping with four different kinds of functions. Moreover, the algorithm is favorably utilized in feature selection and three constrained engineering construction problems. Simulations have shown that the GCLPSO is capable of effectively dealing with constrained problems and solves the problems encountered in actual production.

Enhanced Differential Crossover and Quantum Particle Swarm Optimization for IoT Applications

“…We initially present the comparison of proposed algorithm, EDCQPSO, with others through 30 classic benchmark functions in IEEE CEC2017 [46], as shown in Table 1. The performance of our algorithm on benchmark functions was verified.…”

“…The comparison of mean values and standard deviation after thirty iterations on thirty benchmark functions are listed. Table 4 shows that EDCQPSO ranks first, followed sequentially by GWO-GOA, GHO, GWO, IWO, EBFO, and DA, based on overall rank for CE01-CE30 functions of CEC2017 [46]. On three unimodal test functions (CE01-CE03), EDCQPSO performs better than other algorithms.…”

Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation

“…As far as swarm intelligence optimization algorithms are concerned, a number of related algorithms have been proposed, including grey wolf optimization (GWO) [ 55 ], moth-flame optimization (MFO) [ 56 ], PSO [ 57 ], sine cosine algorithm (SCA) [ 58 ], whale optimizer (WOA) [ 59 ], multi-verse optimizer (MVO) [ 60 ], Harris hawks optimization (HHO) 1 [ 61 ], slime mould algorithm (SMA) 2 [ 62 ], hunger games search (HGS) 3 [ 63 ], Runge Kutta optimizer (RUN) 4 [ 64 ], modified SCA (m_SCA) [ 65 ], boosted GWO (OBLGWO) [ 66 ], opposition-based SCA (OBSCA) [ 67 ], A-C parametric WOA (ACWOA) [ 68 ], biogeography-based learning PSO (BLPSO) [ 69 ], comprehensive learning PSO (CLPSO) [ 70 ], moth-flame optimizer with sine cosine mechanisms (SMFO) [ 71 ], enhanced comprehensive learning particle swarm optimizer (GCLPSO) [ 72 ], enhanced GWO with a new hierarchical structure (IGWO) [ 73 ], improved WOA (IWOA) [ 74 ], and ant colony optimization (ACO) for continuous domains (ACOR) [ 75 ]. Notably, it is well known that ACO [ 76 , 77 ] is an algorithm for solving discrete optimization problems, whereas ACOR can be used to solve optimization problems other than discrete ones.…”

Towards Precision Fertilization: Multi-Strategy Grey Wolf Optimizer Based Model Evaluation and Yield Estimation

“…com/HHO.html, accessed on 28 August 2021) [81,103,104], genetic algorithm (GA) [105], chaotic BA (CBA) [106], multi-verse optimizer (MVO) [107], cuckoo search via Lévy flights (CS) [108], firefly algorithm (FA) [109], salp swarm algorithm (SSA) [110,111], gravitational search algorithm (GSA) [112], ant colony optimization (ACO) [72,113,114], krill herd algorithm (KHA) [115], artificial bee colony (ABC) [116]. Meanwhile, there are many corre-sponding improvement algorithms [70,117], such as enhanced comprehensive learning particle swarm optimization (GLOPSO) [118], chaotic moth-flame optimization (CMFO) [91], hybridizing grey wolf optimization (HGWO) [119], balanced whale optimization algorithm (BWOA) [120], double adaptive random spare reinforced whale optimization algorithm (RDWOA) [121], chaotic mutative moth-flame-inspired optimizer (CLSGMFO) [122], orthogonal learning sine cosine algorithm (OLSCA) [88], multi-strategy enhanced sine cosine algorithm (MSCA) [123], enhanced whale optimizer with associative learning (BM-WOA) [124], enhanced moth flame optimization (SMFO) [125], ant colony optimizer with random spare strategy and chaotic intensification strategy (RCACO) [126], etc.…”