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ABC is an optimization technique, used in finding the best solution from all feasible solutions. However, there is still an insufficiency in ABC regarding improvement in exploitation and convergence speed. In order to improve the performance of ABC we used mean mutation operator (MMO), which uses a linear combination of Gaussian and Cauchy distributions. This convoluted distribution produces larger...
Handling multimodal functions is a very important and challenging task in evolutionary computation community, since most of the real-world applications exhibit highly multi-modal landscapes. Motivated by the dynamics and the proximity characteristics of Differential Evolution's mutation strategies tending to distribute the individuals of the population to the vicinity of the problem's minima, we introduce...
Bacterial foraging optimization (BFO) algorithm is one of the newest nature inspired optimization algorithm, based on social foraging behavior of Escherichia coli. However, this swarm-based algorithm is computationally expensive due to the slow nature of the collective intelligence of bacterial swarm. This paper presents a novel way to accelerate BFO. The novel bacterial foraging oriented by differential...
Differential Evolution (DE) algorithm has been shown to be powerful for many real optimization problems. In traditional DE and its variant algorithms, there are three or more candidates used in the step of mutation, which is one of the three typical operation steps: mutation, crossover and selection. To simplify the mutation, we propose a novel evolution algorithm which needs only two individuals...
In recent years, the Particle Swarm Optimization has rapidly gained increasing popularity and many variants and hybrid approaches have been proposed to improve it. Motivated by the behavior and the proximity characteristics of the social and cognitive experience of each particle in the swarm, we develop a hybrid approach that combines the Particle Swarm Optimization and the Differential Evolution...
This paper presents a component-based model with a novel ranking method (CMR) for constrained evolutionary optimization. In general, many constraint-handling technique inevitably solve two important problems: (1) how to generate the feasible solutions, (2) how to direct the search to find the feasible optimal solution. For the first problem, this paper introduces a component-based model. The model...
This paper presents a novel method to discover promising regions in a continuous search space. Using machine learning techniques, the algorithm named smart sampling was tested in hard known benchmark functions, and was able to find promising regions with solutions very close to the global optimum, significantly decreasing the number of evaluations needed by a metaheuristic to finally find this global...
Differential evolution is a well-known optimization technique to deal with nonlinear and complex problems. However, it suffers from some difficulties, such as expensive computation, problem-dependent parameters, etc. In order to tackle these problems, this paper presents a hybrid DE algorithm, called SAODE, by employing opposition-based learning (OBL) and a self-adapting mechanism to adjust parameters...
An improved evolution algorithm (IEA) is proposed in this paper. It has some new features: 1) using multi-parent search strategy and stochastic ranking strategy and a simple diversity rules to maintain the diversity of the population; 2) using a hybrid self-adaptive crossover-mutation operator, which can enhance the search ability and exploit the optimum offspring; The algorithm of this paper is tested...
An improved quantum-inspired evolutionary algorithm is presented in this paper. Quantum angle is adopted to present the quantum bit in the proposed algorithm. A novel quantum rotation gate strategy is adopted to adjust the direction of the quantum gate which is used to update the quantum population. The step size is adaptively adjusted rather than a fixed angle. Furthermore, the particle swarm optimization...
In studies that compare the performance of population-based optimization algorithms, it is sometimes assumed that the comparison is valid as long as the number of function evaluations is equal, even if the population size differs. This paper shows that such comparisons are invalid. The performance of two algorithms: differential evolution (DE) and global best particle swarm optimization (gbest PSO)...
Although there exist a lot of approaches to solve constrained optimization problem, few of them makes use of the knowledge obtained in the searching process. In the paper, a new algorithm called nearest neighbor evolutionary algorithm (NNE) is proposed to solve the constrained optimization problem. NNE not only performs global search and local search in the searching process, but also considers the...
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