Many optimisation problems are dynamic in the sense that changes occur during the optimisation process, and therefore are more challenging than the stationary problems. To solve dynamic optimisation problems, the prop...
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Many optimisation problems are dynamic in the sense that changes occur during the optimisation process, and therefore are more challenging than the stationary problems. To solve dynamic optimisation problems, the proposed approaches should not only attempt to seek the global optima but be able to also keep track of changes in the track record of landscape solutions. In this research work, one of the most recent new population-based meta-heuristic optimisation technique called a harmony search algorithm for dynamic optimization problems is investigated. This technique mimics the musical process when a musician attempts to find a state of harmony. In order to cope with a dynamic behaviour, the proposed harmony search algorithm was hybridised with a (i) random immigrant, (ii) memory mechanism and (iii) memory based immigrant scheme. The performance of the proposed harmony search is verified by using the well-known dynamic test problem called the Moving Peak Benchmark (MPB) with a variety of peaks. The empirical results demonstrate that the proposed algorithm is able to obtain competitive results, but not the best for most of the cases, when compared to the best known results in the scientific literature published so far.
Swarm intelligence algorithms are amongst the most efficient approaches toward solving optimizationproblems. Up to now, most of swarm intelligence approaches have been proposed for optimization in static environments...
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Swarm intelligence algorithms are amongst the most efficient approaches toward solving optimizationproblems. Up to now, most of swarm intelligence approaches have been proposed for optimization in static environments. However, numerous real-world problems are dynamic which could not be solved using static approaches. In this paper, a novel approach based on artificial fish swarm algorithm (AFSA) has been proposed for optimization in dynamic environments in which changes in the problem space occur in discrete intervals. The proposed algorithm can quickly find the peaks in the problem space and track them after an environment change. In this algorithm, artificial fish swarms are responsible for finding and tracking peaks and several behaviors and mechanisms are employed to cope with the dynamic environment. Extensive experiments show that the proposed algorithm significantly outperforms previous algorithms in most of tested dynamic environments modeled by moving peaks benchmark.
In dynamic environments, it is difficult to track a changing optimal solution over time. An improved univariate marginal distribution algorithm(IUMDA) is proposed to deal with dynamic optimization problems. This appro...
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In dynamic environments, it is difficult to track a changing optimal solution over time. An improved univariate marginal distribution algorithm(IUMDA) is proposed to deal with dynamic optimization problems. This approach is composed of the diffusion model, which uses the information of current population, and the inertia model, which uses the part history information of the optimal solution. After an environment changed, the strategy is changed by a detecting operator to guide increasing the population diversity. Finally an experimental study on dynamic sphere function was carried out to compare the performance of IUMDA and mutation UMDA. The experimental results show that the IUMDA is effective for the function with moving optimum and can adapt the dynamic environments rapidly.
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