Babak Nasiri

A Gaussian Firefly Algorithm

Firefly algorithm is one of the evolutionary optimization algorithms, and is inspired by fireflies behavior in nature. Each firefly movement is based on absorption of the other one. In this paper to stabilize fireflys movement, it is proposed a new behavior to direct fireflies movement to global best if there was no any better solution around them. In addition to increase convergence speed it is proposed to use Gaussian distribution to move all fireflies to global best in each iteration. Proposed algorithm was tested on five standard functions that have ever used for testing the static optimization algorithms. Experimental results show better performance and more accuracy than standard Firefly algorithm.

A novel multi-swarm algorithm for optimization in dynamic environments based on particle swarm optimization

Optimization in dynamic environment is considered among 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 approach, in which several mechanisms were employed to face the challenges in this domain. In this algorithm, an improved multi-swarm approach has been used for finding peaks in the problem space and tracking them after an environment change in an appropriate time. Moreover, a novel method based on change in velocity vector and particle positions was proposed to increase the diversity of swarms. For improving the efficiency of the algorithm, a local search based on adaptive exploiter particle around the best found position as well as a novel awakening-sleeping mechanism were utilized. The experiments were conducted on Moving Peak Benchmark which is the most well-known benchmark in this domain and results have been compared with those of the state-of-the art methods. The results show the superiority of the proposed method.

mNAFSA: A novel approach for optimization in dynamic environments with global changes

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 order to eliminate weak points of standard AFSA and increase convergence speed of the algorithm. Second, a multi-swarm algorithm based on NAFSA (mNAFSA) was presented to conquer particular challenges of dynamic environment by proposing several novel mechanisms including particularly modified multi-swarm mechanism for finding and covering potential optimum peaks and diversity increase mechanism which is applied after detecting an environment change. The proposed approaches have been evaluated on moving peak benchmark, which is the most prominent benchmark in this domain. This benchmark involves several parameters in order to simulate different configurations of dynamic environments. Extensive experiments show that the proposed algorithm significantly outperforms previous algorithms in most of the tested dynamic environments modeled by moving peaks benchmark.

A new artificial fish swarm algorithm for dynamic optimization problems

Artificial fish swarm algorithm is one of the swarm intelligence algorithms which performs based on population and stochastic search contributed to solve optimization problems. This algorithm has been applied in various applications e.g. data clustering, neural networks learning, nonlinear function optimization, etc. Several problems in real world are dynamic and uncertain, which could not be solved in a similar manner of static problems. In this paper, for the first time, a modified artificial fish swarm algorithm is proposed in consideration of dynamic environments optimization. The results of the proposed approach were evaluated using moving peak benchmarks, which are known as the best metric for evaluating dynamic environments, and also were compared with results of several state-of-the-art approaches. The experimental results show that the performance of the proposed method outperforms that of other algorithms in this domain.

History-driven firefly algorithm for optimisation in dynamic and uncertain environments

Due to dynamic and uncertain nature of many optimisation problems in real-world, the applied algorithm in this environment must be able to continuously track the changing optima over time. In this paper, we report a novel speciation-based firefly algorithm for dynamic optimisation, which improved its performance by employing prior landscape historical information. The proposed algorithm, namely history-driven speciation-based firefly algorithm HdSFA, uses a binary space partitioning BSP tree to capture the important information about the landscape during the optimisation process. By utilising this tree, the algorithm can approximate the fitness landscape and avoid wasting the fitness evaluation for some unsuitable solutions. The proposed algorithm is evaluated on the most well-known dynamic benchmark problem, moving peaks benchmark MPB, and also on a modified version of it, called MPB with pendulum-like motion among the environments PMPB, and its performance is compared with that of several state-of-the-art algorithms in the literature. The experimental results and statistical test prove that HdSFA outperforms most of the algorithms in different scenarios.

History-Driven Particle Swarm Optimization in dynamic and uncertain environments

Due to dynamic and uncertain nature of many optimization problems in real-world, an algorithm for applying to this environment must be able to track the changing optima over the time continuously. In this paper, we report a novel multi-population particle swarm optimization, which improved its performance by employing an external memory. This algorithm, namely History-Driven Particle Swarm Optimization (HdPSO), uses a BSP tree to store the important information about the landscape during the optimization process. Utilizing this memory, the algorithm can approximate the fitness landscape before actual fitness evaluation for some unsuitable solutions. Furthermore, some new mechanisms are introduced for exclusion and change discovery, which are two of the most important mechanisms for each multi-population optimization algorithm in dynamic environments. The performance of the proposed approach is evaluated on Moving Peaks Benchmark (MPB) and a modified version of it, called MPB with pendulum motion (PMPB). The experimental results and statistical test prove that HdPSO outperforms most of the algorithms in both benchmarks and in different scenarios.

Improved Speciation-Based Firefly Algorithm in Dynamic and Uncertain Environments

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 performance of the algorithm, multiple adaptation techniques have been used such as adapting the number of species, number of fireflies in each specie and number of active fireflies in each specie. A set of experiments are conducted to study the performance of the proposed algorithm on Moving Peaks Benchmark (MPB) which is currently the most well-known benchmark for evaluating algorithm in dynamic environments. The experimental results indicate that the proposed algorithm statistically performs better than several state-of-the-art algorithms in terms of offline-error.