Many practical, real-world applications have dynamic features. If the changes in the fitness function of an optimization problem are moderate, a complete restart of the optimization algorithm may not be warranted. In those cases, it is meaningful to apply optimization algorithms that can accommodate change. In the recent past, many researchers have contributed algorithms suited for dynamic prob...

Many practical, real-world applications have dynamic features. If the changes in the fitness function of an optimization problem are moderate, a complete restart of the optimization algorithm may not be warranted. In those cases, it is meaningful to apply optimization algorithms that can accommodate change. In the recent past, many researchers have contributed algorithms suited for dynamic prob...

Artificial fish swarm algorithm (AFSA) is one of the state-of-the-art swarm intelligence algorithms that is widely used for optimization purposes in static environments. However, numerous real-world problems are dynamic and uncertain, which could not be solved using static approaches. The contribution of this paper is twofold. First, a novel AFSA algorithm, so called NAFSA, has been proposed in...

nowadays, it is common to find optimal point of the dynamic problem; dynamic problems whose optimal point changes over time require algorithms which dynamically adapt the search space instability. in the most of them, the exploitation of some information from the past allows to quickly adapt after an environmental change (some optimal points change). this is the idea underlining the use of memo...

Many optimization problems in real world are dynamic and they are changing over time. For resolving these problems, many different algorithms have been proposed. One of these, is PSO algorithm which has well supported its ability in resolving static problems. But this algorithm has some problems in dynamic environments. In this paper, an improved PSO algorithm with inertia parameter has been pr...

e-learning model is examined of three major dimensions. and each dimension has a range of indicators that is effective in optimization and modeling, in many optimization problems in the modeling, target function or constraints may change over time that as a result optimization of these problems can also be changed. if any of these undetermined events be considered in the optimization process, t...

Many real-world problems are dynamic and require an optimization algorithm that is able to continuously track a changing optimum over time. In this paper, a new multiagent algorithm is proposed to solve dynamic problems. This algorithm is based on multiple trajectory searches and saving the optima found to use them when a change is detected in the environment. The proposed algorithm is analyzed...

Dynamic optimization problems (DOPs) are those whose specifications change over time during the optimization, resulting in continuously moving optima. Most research work on DOPs is based on the assumption that the goal of addressing DOPs is to track the moving optima. In this paper, we first point out the practical limitations on tracking the moving optima. We then propose to find optimal solut...

In this study, the modification of quantum multi-swarm optimization algorithm is proposed for dynamic problems. The implies using search operators from differential evolution with a certain probability within particle swarm to improve algorithm’s capabilities in dynamically changing environments. For testing, Generalized Moving Peaks Benchmark was used. experiments were performed four benchmark...