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Hardware-software (HW-SW) partitioning plays a vital role in design phase of embedded system. The partitioning is a process to map each computation task in an application to either software or hardware. In general, hardware run faster compared to software, but with significant cost and resources utilization. Thus, current embedded system often incorporates a mix of hardware and software component...
In this paper, the scatter search is modified and combined with the variable-sample technique to deal with the simulation-based optimization problem. First, a new design of scatter search is proposed to deal with the deterministic global optimization problem. Then, the variable-sample technique is combined with the modified scatter search method in order to compose a new global search method that...
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 location of the micro fractures developed during hydraulic stimulation in unconventional reservoirs can be posed as a non-linear optimization problem. We propose a Differential Evolution algorithm (DE) to estimate the position of the microseismic events. A geometry model representing the subsurface is used to construct the cost function to be optimized. We examine two kind of tests, one using...
The large-scale electric vehicles access to the distribution network, the randomness and decentralization of the distribution network management put forward new requirements for the transmission line load capacity also launched a challenge. In order to make the distribution network of electric vehicles take safe, economic and stable operation, according to the characteristics of electric vehicles,...
In order to solve the problem of great memory usage when merging pattern sets into Deterministic Finite Automaton (DFA), an important method is that it divides n regular expressions into m groups in reasonable ways. Through combining minimum interactive rate grouping strategy with the advantage of global search abilities of optimization of artificial fish school algorithm (ASFA), one grouping algorithm...
In this paper, a novel firefly algorithm (FA) is presented to reduce the dependency on parameters. The new FA algorithm is called dynamic step factor based FA (DSFFA), in which the step factor is not fixed and it is dynamically updated during the evolution. Experimental study on several classical benchmark functions show that DSFFA is superior to the basic FA and three other FAs.
Differential evolution (DE) is an efficient and robust evolutionary algorithm, which has been widely and successfully applied to solve global optimization problems. Although many methods have been developed based on the population topology to improve the performance of DE, the effects of population topology interacted with the functions being optimized are not considered in most of the algorithm designs...
This paper presents a new interval optimization algorithm (ESIA) combining interval algorithm with evolution strategy in bionics., to improve the search efficiency and make the accelerated tool constructed easier comparing with the traditional interval algorithm (IA), hence it can be applied to high dimensional optimization problems better. The ESIA employed the evaluation strategy to construct accelerated...
In this paper, we introduce a computational approach for solving optimal driving strategies for trains in which the controllers action are confined to select from discrete modes. Since the controllers operate in succession, in order to avoid the undesirable Zeno phenomenon, we assume that the only one controller can be selected at a time and each active controller duration requires a minimum non-negligible...
In this paper, a novel particle swarm optimizer is developed by introducing projection operators described by projection matrices into the algorithm. Under the projection operators, the particles will oscillate along the directions determined by the projection operators to enhance global explorations. At the same time, the particles explore locally the optimal solutions when they are close to the...
Biogeography-based optimization (BBO) is a new biogeography inspired algorithm for global optimization, which has shown impressive performance on many popular benchmarks. However, there are still some open research aspects that need to be addressed for BBO. In this paper, we extend the original BBO to iCPBBOCO algorithm (an improved Cosine-Probability chaotic BBO). Furthermore, in order to further...
Bandit based optimisation schemes have a remarkable advantage over gradient based approaches due to their global perspective, which eliminates the danger of getting stuck at local optima. However, for continuous optimisation problems or problems with a large number of actions, bandit based approaches can be hindered by slow learning. Gradient based approaches, on the other hand, navigate quickly in...
This paper introduces the relative principium of K-Means algorithm, simulated annealing (SA) algorithm and particle swarm optimization (PSO) algorithm at first. Then, in allusion to the influence of the initial value of the K-Means algorithm on the optimal solution of the algorithm, a hybrid algorithm of K-Means based on SA-PSO is proposed. The new algorithm uses the advantage of jumping out of local...
In evolutionary algorithms, it is difficult to balance the exploration and exploitation. Usually, global search is utilized to find promising solutions, and local search is beneficial to the convergence of the solutions in the population. Combing different search strategies is a promising way to take advantages of different methods. Following the idea of DE/EDA, this paper proposes another way to...
For the past few years, many swarm intelligent optimization methods have been proposed. In this article, a new optimization algorithm based on gravitational search algorithm and chaos is introduced. In the new algorithm, chaotic operator is used by population initialization. Population would be regenerated when premature of algorithm. The new algorithm has been compared with some optimization methods...
Particle swarm optimization (PSO) is a heuristic stochastic evolutionary algorithm. However, standard PSO exists unbalanced exploitation and exploration, lower convergence speed. An improved technique is introduced into the standard PSO with adaptive computation of the inertia weights. After every iteration, a new competition with a random swarm is operated to jump out of the local optimum. Four benchmark...
Most of the hybrid algorithms are produced by combination of a global optimization algorithm with a local search algorithm. In this paper, the focus is on a simple and efficient hybrid algorithm by combining the differential evolution(DE) and the Nelder-Mead simplex(NM) variant for solving global optimization problem. The DE explores the whole search space to find some promising areas and then the...
This paper proposes a hybrid evolutionary algorithm based on DE and TLBO (HDT). The HDT algorithm independently evolved the two sub populations of DE and TLBO to preserve the diversity of population. In every generation of evolution, we first get a composite individual from current best individuals of the two sub populations, then we use search guide of composite individual to remain the variety of...
In this paper, a new auxiliary function is proposed for large scale global optimization. For the problems with a lot of local optimal solutions, the proposed auxiliary function can eliminate all local optimal solutions no better than those obtained local optimal solution and find a better local optimal solution. To enhance efficiency and effectiveness of evolutionary algorithms, the proposed auxiliary...
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