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In this paper, a particle swarm optimization method with a new strategy for inertia weight has been considered. The author abandoned the commonly used linear inertia weight and proposed a new dynamic inertia weight based on fitness of the particles. The new weight is a function of the best and the worst fitness of the particles. The considered NIWPSO algorithm was tested on a set of benchmark functions...
In this paper, a parallel computing implementation of a multiagent coordination optimization (MCO) algorithm is introduced using $\mathtt {parfor}$ , a built-in MATLAB function. As a novel variation of particle swarm optimization (PSO), the MCO algorithm has demonstrated significant performance improvement, in terms of both accuracy and efficiency, in solving real-time optimization problems when...
This paper aims to tackle the shortcomings of the standard artificial bee colony algorithm (ABC) such as slow convergence, long solving time and being easy to fall into local optima. We study the state transformation formula and propose a parallelized ABC algorithm with Message Passing Interface (MPI). We use the traveling salesman problem (TSP) as the case study. Our experiments show that the parallel...
This paper proposed a novel bio-inspired optimizer, namely the root system growth algorithm (RSGA), which adopts the root foraging, memory and communication, and auxin-regulated mechanisms of the root system. When tested against benchmark functions, the RSGA markedly outperforms the CMA-ES, PSO, GA, and DE algorithms in terms of accuracy, robustness and convergence speed.
Gravitational Search Algorithm (GSA) is a population-based optimization algorithm based on Newton's law of gravity and the notion of mass interactions. GSA has the advantage of proper global search ability. However, it suffers from weak local search due to relatively big step-size of agents in the search process. In order to improve the balance between exploration and exploitation of GSA, two mechanisms...
The Group Search Optimizer(GSO) is a novel optimization algorithm, which is inspired by searching behavior of animals. In this paper, we proposed an improved GSO algorithm named Fast Global Group Search Optimizer(FGGSO) to increase searching speed and balance the exploitation and exploration of the algorithm, which is based on our previous works. At first time, considering the complexity and time-consuming...
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