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Quantum-behaved particle swarm optimization (QPSO) is a novel variant of particle swarm optimization (PSO), inspired by quantum mechanics. Compared with traditional PSO, the QPSO algorithm guarantees global convergence and has less number of controlling parameters. However, QPSO is likely to get trapped into a local optimum because of using a single search strategy. This paper proposes a cooperative...
The bare bones particle swarm optimization (BBPSO) is a population-based algorithm. The BBPSO is famous for easy coding and fast applying. A Gaussian distribution is used to control the behavior of the particles. However, every particle learning from a same particle may cause the premature convergence. To solve this problem, a new hierarchical bare bones particle swarm optimization algorithm is proposed...
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...
With NVIDA Tegra Jetson X1 and Pascal P100 GPUs, NVIDIA introduced hardware-based computation on FP16 numbers also called half-precision arithmetic. In this talk, we will introduce the steps required to build a viable benchmark for this new arithmetic format. This will include the connections to established IEEE floating point standards and existing HPC benchmarks. The discussion will focus on performance...
Particle Swarm Optimization (PSO) is fast and popular algorithm to find the optimum value of non-linear and multi-dimensional function. However, it often easily trapped into local optima because the particles move closer to the best particle quickly. This paper purposes a new algorithm called Multi-Group Particle Swarm Optimization with Random Redistribution (MGRR-PSO) that tried to solve the weakness...
The Cuckoo Search Algorithm (CSA) is a promising metaheuristic algorithm. It is applied to solve many problems in different fields. This paper proposes a new cuckoo search algorithm by combining the cuckoo search algorithm with the Hill Climbing method for solving the integer and minimax optimization problems. The proposed algorithm is named hybrid cuckoo search and hill climbing (CSAHC). CSAHC starts...
Bare bones particle swarm optimization (BBPSO) algorithm, a swarm intelligence algorithm, is famous for its easy applying and parameter-free. That is why its principles and applications have been studied by a IoT of scholars in recent years. However, quickly losing the diversity of the swarm still causes the premature convergence in the iteration process. Hence, a pair-wise bare bones particle swarm...
Sine cosine algorithm (SCA) is one of the most recent population-based optimization algorithms proposed for solving optimization problems. In the present study, in order to improve the performance of this algorithm, a new weighted update position mechanism (WUPM) was employed instead of the position update method of search agents in SCA. In the proposed method, in addition to a position and fitness,...
This paper aims to introduce an algorithm by performing three modifications in the GCS algorithm in order to enhance the rate of convergence using globally best solutions. The Cuckoo Search Algorithm, CSA, which has developed recently, is an optimization method that uses random equations for search engine and therefore the rate of convergence is considerably low. The improved algorithm developed,...
Traditional differential evolution (DE) algorithm has a tendency to suffer from premature convergence. In this paper, we proposed an improved DE based on dynamic mutation operator and opposition learning strategy. These mechanisms can expand the search area and is helpful to balance exploration and exploitation of DE. Numerical experiments demonstrate that our algorithm is effective.
Grey wolf optimizer (GWO) is a recently proposed intelligent optimization method inspired by hunting behavior of grey wolves. In GWO algorithm, the parameter of a⃗ is decreased from 2 to 0 to balance exploitation and exploration, respectively. A novel time-varying parameter of a⃗ decreasing linearly is used to enhance the performance of GWO algorithm. In order to enhance the global convergence, when...
Bare-bones particle swarm optimization (BPSO) converges quickly and is parameter-free. But BPSO is easily suffer from the premature convergence. This paper presents a distribution-guided BPSO (DBPSO) in which an adaptive jump operation is introduced to help the particle get out of the local optimal and each dimension of the particle is assigned a jump probability according to the evolutionary state...
Firefly algorithm (FA), first put forward by a Cambridge scholar Yang, is a kind of swarm intelligent algorithm imitating nature fireflies' predation and courtship behaviour. It has been widely studied and gradually applied to different engineering fields. However, the basic FA has several shortcomings such as easiness to fall into local optimum, premature convergence, et al. Recent years have witnessed...
Differential evolution (DE) is a high performance and easy to implement evolutionary algorithm. The DE algorithm with small population size (i.e., micro-DE) can further increase the efficiency of the algorithm. However, it also decreases its exploration capability, causing stagnation and pre-mature convergence. In this paper, the idea of exploration enhancement at the mutation level is proposed. The...
The firefly algorithm is a stochastic meta-heuristic algorithm that incorporates randomness into a search process. In essence, the randomness is useful when determining the next point in the search space and therefore has a crucial impact when exploring the new solution. Simultaneously, randomized mechanism plays an important role in balance the exploration and exploitation during the process. In...
Inspired by Gaussian barebones differential evolution (GBDE), this study attempts to propose a new Gaussian mutation strategy, termed by GBDE/best-rand, to improve the solution accuracy. This study also proposes a hybrid crossover strategy, the hybridization of the binomial and arithmetic crossover strategies, for differential evolution (DE) to further balance the global search ability and convergence...
State transition algorithm has been emerging as a new intelligent global optimization method in recent few years. The standard continuous STA has demonstrated powerful global search ability for global optimization problems whose dimension is no more than 100. In this study, we give a test report to present the performance of standard continuous STA for large scale global optimization when compared...
In order to balance the exploitation and exploration, a self-adaptive mutation cuckoo search algorithm was proposed in this paper. On the one hand, the adjustment of search step according to the distance between current and optimal nests was adopt, which is beneficial to improve convergence speed. On the other hand, the self-adaptive discovery rate was introduced to increase flexibility of algorithm...
The use of meta-heuristic algorithms for solving real world problems increases day by day. Bat Algorithm is a meta-heuristic optimization algorithm based on the echolocation behavior of microbats. Bat Algorithm has advantage which claimed to provide very quick convergence at a very initial stage by automatic switching from exploration to exploitation. Hereby, algorithm loses exploration capability...
The idea of particle swarm optimization falls under the domain of swarm intelligence. Particle swarm optimization technique is widely used for finding the global minima of well-known benchmark functions. The main idea behind this technique is that working in a group improves the performance of a system. A modified particle swarm optimization technique is proposed in this paper and tested on seven...
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