Abstract
In the last decade, many evolutionary computation (EC) algorithms with diversity enhancement have been proposed to solve large-scale optimization problems in big data era. Among them, the social learning particle swarm optimization (SLPSO) has shown good performance. However, as SLPSO uses different guidance information for different particles to maintain the diversity, it often results in slow convergence speed. Therefore, this article proposes a new region encoding scheme (RES) to extend the solution representation from a single point to a region, which can help EC algorithms evolve faster. The RES is generic for EC algorithms and is applied to SLPSO. Based on RES, a novel adaptive region search (ARS) is designed to on the one hand keep the diversity of SLPSO and on the other hand accelerate the convergence speed, forming the SLPSO with ARS (SLPSO-ARS). In SLPSO-ARS, each particle is encoded as a region so that some of the best (e.g., the top P) particles can carry out region search to search for better solutions near their current positions. The ARS strategy offers the particle a greater chance to discover the nearby optimal solutions and helps to accelerate the convergence speed of the whole population. Moreover, the region radius is adaptively controlled based on the search information. Comprehensive experiments on all the problems in both IEEE Congress on Evolutionary Computation 2010 (CEC 2010) and 2013 (CEC 2013) competitions are conducted to validate the effectiveness and efficiency of SLPSO-ARS and to investigate its important parameters and components. The experimental results show that SLPSO-ARS can achieve generally better performance than the compared algorithms.
Highlights
In recent years, the evolutionary computation (EC) algorithms have been successful in solving various global optimization problems, such as the works in particle swarm optimization (PSO) [1]-[5], ant colony optimization (ACO) [6]-[9], genetic algorithm (GA) [10]-[13], estimation of distribution algorithm (EDA) [14][15], differential evolution (DE) [16]-[20], and some other algorithms [21][22]
In order to accelerate the convergence speed of social learning particle swarm optimization (SLPSO), we propose to encode the solution by the region encoding scheme (RES) and to execute the novel local search strategy named adaptive region search (ARS) to further improve the algorithm, forming the SLPSO with ARS (SLPSO-ARS)
From Table S.VI in the supplemental material, the p-values show that SLPSO-ARS performs significantly better than CSO, DMS-L-PSO, CCPSO2, DECC-G, and DECC-differential grouping (DG)
Summary
Where t is the current generation index; ω is inertia weight [24]; c1 and c2 are acceleration coefficients [23]; r1d and r2d are two random numbers generated uniformly distributed in [0, 1] for the dimension d; pbesti (personal best) and gbest (global best) are the best position vector found so far by particle i and all particles, respectively. The SLPSO can maintain diversity while the RES can help evolve faster by the novel RES and by using a local search strategy named adaptive region search (ARS) based on the RES to accelerate the convergence speed. We propose to use RES to encode a particle as a region rather than only a single point in this paper This way, we can carry out additional operations (e.g., the local search) with the help of the region information to generate more solutions in every generation.
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