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Differential evolution (DE) has been proven to be a simple and powerful evolutionary algorithm, and obtains many successful applications in scientific and engineering fields. The mutation strategy plays the key role in DE for finding global optimal solutions. In most of the DE algorithms, the base and difference vectors are randomly selected from the current population. Furthermore, both the neighborhood...
Allreduce is a basic building block for parallel computing. Our target here is "Big Data" processing on commodity clusters (mostly sparse power-law data). Allreduce can be used to synchronize models, to maintain distributed datasets, and to perform operations on distributed data such as sparse matrix multiply. We first review a key constraint on cluster communication, the minimum efficient...
In this paper, by combination of some approaches we propose a new approach of Differential Evolution (DE) algorithm, called DE with nonlinear simplex method and dynamic neighborhood search (DENNS). In our approach the nonlinear simplex method (NSM) is used for population initialization and local neighborhood search. Moreover, local and global neighborhood search operators are employed to generate...
Many practical optimization problems are constrained and have a bounded search space. In this paper, we propose and compare a wide variety of bound handling techniques for particle swarm optimization. By examining their performance on flat landscapes, we show that many bound handling techniques introduce significant search bias. Furthermore, we compare the performance of many bound handling techniques...
This work presents a fast and accurate congestion analysis tool at the global routing stage. It focuses on capturing the difficult-to-solve congestion in global routing designs. The proposed framework identifies the routing congestion using a novel Orthogonal Congestion Correlation (OCC) factor, which identifies the hard-to-route hot-spots. A key contribution of this work is a fast global router to...
The vector evaluated particle swarm optimisation (VEPSO) algorithm is a multi-swarm variation of particle swarm optimisation (PSO) used to solve static multi-objective optimisation problems (SMOOPs). Recently, VEPSO was extended to the dynamic VEPSO (DVEPSO) algorithm to solve dynamic multi-objective optimisation problems (DMOOPs) that have at least one objective that changes over time. The search...
Numerous variations of Particle Swarm Optimization (PSO) algorithms have been recently developed, with the best aim of escaping from local minima. One of these recent variations is PSO-LA model which employs a Learning Automata (LA) that controls the velocity of the particle. Another variation of PSO enables particles to dynamically search through global and local space. This paper presents a Dynamic...
The Kronecker product, also called tensor product, is a fundamental matrix algebra operation, which is widely used as a natural formalism to express a convolution of many interactions or representations. Given a set of matrices, we need to multiply their Kronecker product by a vector. This operation is a critical kernel for iterative algorithms, thus needs to be computed efficiently. In a previous...
The influence of parallelism on the performance of competitive adaptive differential evolution is studied. Two serial competitive differential evolution variants described in literature and sixteen novel parallel variants were experimentally compared. All the parallel differential evolution variants in this study are based on a migration model with the star topology. The algorithms were compared on...
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