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Loop invariant generation is a fundamental problem in program analysis and verification. In this work, we propose a new approach to automatically constructing inductive loop invariants. The key idea is to aggressively squeeze an inductive invariant based on Craig interpolants between forward and backward reachability analysis. We have evaluated our approach by a set of loop benchmarks, and experimental...
Large scale optimization has become a well-recognised field in many science and engineering applications and a variety of metaheuristic algorithms adopting cooperative coevolution (CC) framework with problem decomposition have been applied to solve them. In this paper, a novel decomposition strategy termed as random selection is proposed. In random selection strategy, only a small part of decision...
JavaScript is a dynamic programming language that has been widely used nowadays. The dynamism has become a hindrance of type analysis for JavaScript. Existing works use either static or dynamic type analysis to infer variable types for JavaScript. Static type analysis of JavaScript is difficult since it is hard to predict the behavior of the language without execution. Dynamic type analysis is usually...
An opposition-based learning competitive particle swarm optimizer (OBL-CPSO) is proposed to address the problem of premature convergence in PSO. Two learning mechanisms have been employed in OBL-CPSO, which are competitive learning from competitive swarm optimizer (CSO) and opposition-based learning. In each iteration of OBL-CPSO, the competitive learning works among three randomly selected particles...
This paper introduces a variant of particle swarm optimization algorithm called the drift particle swarm optimization (DPSO), which is inspired by the free electron model in an external electric field at finite temperature. As the compression-expansion coefficient in DPSO is an important parameter which can greatly influence the performance of the algorithm, three types of control strategies are proposed...
Quantum-behaved particle swarm optimization was proposed from the view of quantum world and based on the particle swarm optimization, which has been proved to outperform the traditional PSO. The Expansion-Contraction coefficient is the only parameter in QPSO, which has great influence on the global search ability and convergence of the particles. In this paper, two parameter control methods are proposed...
This paper describes a variant of quantum-behaved particle swarm optimization (QPSO) algorithm named C-QPSO for solving global optimization problems. In C-QPSO the QPSO is revised by including a novel crossover operator to enhance the algorithm's ability to escape from local optima. The experimental results on test functions demonstrate that the proposed hybrid optimization algorithm performs much...
Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is the algorithm proposed by our group to improve traditional PSOs. Comparing with traditional PSOs, the QPSO has better global-convergence and fewer parameters. Both PSO and QPSO were generally considered to have high requirement for the computing platform; hence, it is thought impractical to employ them into resource-constrained embedded...
Quantum-behaved Particle Swarm Optimization algorithm (QPSO) is a new variant of Particle Swarm Optimization (PSO). It is also a population-based search strategy, which has good performance on well-known numerical test problems. QPSO is based on the standard PSO and inspired by the theory of quantum physics. In this paper, we explore the parallelism of QPSO and implement the parallel QPSO based on...
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