Many realworld optimization problems are dynamic optimization problems (DOPs), where changes may occur over time regarding the objective function, decision variable, and constraints, etc. DOPs raise big challenges to traditional optimization methods as well as evolutionary algorithms (EAs). The last decade has witnessed increasing research efforts on handling dynamic optimization problems using...

In real life we are often confronted with dynamic optimization problems whose optima change over time. These problems challenge traditional optimization methods as well as conventional evolutionary optimization algorithms. In this paper, we propose an evolutionary model that combines the differential evolution algorithm with cellular automata to address dynamic optimization problems. In the pro...

Many real world optimization problems are dynamic, meaning that their optimal solutions are time-varying. In recent years, an effective approach to address these problems has been the multi-swarm PSO (mPSO). Despite this, we believe that there is still room for improvement and, in this contribution we propose two simple strategies to increase the effectiveness of mPSO. The first one faces the d...

Dynamic optimisation problems are difficult to solve because they involve variables that change over time. In this paper, we present a new HookeJeeves based Memetic Algorithm (HJMA) for dynamic function optimisation, and use the Moving Peaks (MP) problem as a test bed for experimentation. The results show that HJMA outperforms all previously published approaches on the three standardised benchm...

This paper investigates optimization in dynamic environments where the numbers of optima are unknown or fluctuating. The authors present a novel algorithm, Dynamic Population Differential Evolution (DynPopDE), which is specifically designed for these problems. DynPopDE is a Differential Evolution based multi-population algorithm that dynamically spawns and removes populations as required. The n...

Many real-world optimization problems are dynamic in nature. The applied algorithms in this environment can pose serious challenges, especially when the search space is multimodal with multiple, time-varying optima. To address these challenges, this paper proposed a speciation-based firefly algorithm to maintain the population diversity in different areas of the landscape. To improve the perfor...

Many realworld optimization problems are dynamic optimization problems (DOPs), where changes may occur over time regarding the objective function, decision variable, and constraints, etc. DOPs raise big challenges to traditional optimization methods as well as evolutionary algorithms (EAs). The last decade has witnessed increasing research efforts on handling dynamic optimization problems using...

Many of the problems considered in optimization and learning assume that solutions exist in a dynamic. Hence, algorithms are required that dynamically adapt with the problem’s conditions and search new conditions. Mostly, utilization of information from the past allows to quickly adapting changes after. This is the idea underlining the use of memory in this field, what involves key design issue...

Optimization in dynamic environment is considered amongst prominent optimization problems. There are particular challenges for optimization in dynamic environments, so that the designed algorithms must conquer the challenges in order to perform an efficient optimization. In this paper, a novel optimization algorithm in dynamic environments was proposed based on particle swarm optimization appro...

Progress in science and engineering relies on the ability to measure, reliably and in detail, pertinent properties of artifacts under design. Progress in the area of database-index design thus relies on empirical studies based on prototype implementations of indexes. This paper proposes a benchmark that targets techniques for the indexing of the current and near-future positions of moving objec...