Sankalap Arora

The Firefly Optimization Algorithm: Convergence Analysis and Parameter Selection

bio-inspired optimization techniques have obtained great attention in recent years due to its robustness, simplicity and efficiency to solve complex optimization problems. The firefly Optimization (FA or FFA) algorithm is an optimization method with these features. The algorithm is inspired by the flashing behavior of fireflies. In the algorithm, randomly generated solutions will be considered as fireflies, and brightness is assigned depending on their performance on the objective function. The algorithm is analyzed on basis of performance and success rate using five standard benchmark functions by which guidelines of parameter selection are derived. The tradeoff between exploration and exploitation is illustrated and discussed.

A conceptual comparison of firefly algorithm, bat algorithm and cuckoo search

There are various mathematical optimization problems that can be effectively solved by metaheuristic algorithms. The advantage of these algorithms is that they perform iterative search processes which efficiently perform exploration and exploitation in the domain space containing local and global optima. In this context, three types of metaheuristic algorithms called firefly algorithm, bat algorithm and cuckoo search algorithm were used to find optimal solutions. Firefly is inspired by behavior of flies, bat algorithm is based on the echolocation behavior of bats while in cuckoo search, a pattern corresponds to a nest and similarly each individual attribute of the pattern corresponds to a cuckoo-egg. A series of computational experiments using each algorithm were conducted. Experimental results were analyzed and it is observed that firefly algorithm seems to perform better than bat algorithm and cuckoo search.

Chaotic grey wolf optimization algorithm for constrained optimization problems

Abstract The Grey Wolf Optimizer (GWO) algorithm is a novel meta-heuristic, inspired from the social hunting behavior of grey wolves. This paper introduces the chaos theory into the GWO algorithm with the aim of accelerating its global convergence speed. Firstly, detailed studies are carried out on thirteen standard constrained benchmark problems with ten different chaotic maps to find out the most efficient one. Then, the chaotic GWO is compared with the traditional GWO and some other popular meta-heuristics viz. Firefly Algorithm, Flower Pollination Algorithm and Particle Swarm Optimization algorithm. The performance of the CGWO algorithm is also validated using five constrained engineering design problems. The results showed that with an appropriate chaotic map, CGWO can clearly outperform standard GWO, with very good performance in comparison with other algorithms and in application to constrained optimization problems.

An improved butterfly optimization algorithm with chaos

Butterfly Optimization Algorithm (BOA) is a new comer in the category of nature inspired metaheuristic algorithms, inspired from food foraging behavior of the butterflies. Similar to other metaheuristic algorithms, it encounters two probable problems; (1) entrapment in local optima and (2) slow convergence speed. Chaotic maps are one of the best methods to improve the performance of metaheuristic algorithms. In the present study, chaos is introduced into BOA which increases its performance in terms of both local optima avoidance and convergence speed. Ten chaotic maps are employed to enhance the performance of the BOA. The proposed chaotic BOAs are validated on unimodal and multimodal benchmark test functions as well as on engineering design problems. The results indicate that the chaotic maps are able to significantly boost the performance of BOA.

Mutated firefly algorithm

In the standard firefly algorithm, every firefly has same parameter settings and its value changes from iteration to iteration. The solutions keeps on changing as the optima are approaching which results that it may fall into local optimum. Furthermore, the underlying strength of the algorithm lies in the attractiveness of less brighter firefly towards the brighter firefly which has an impact on the convergence speed and precision. So to avoid the algorithm to fall into local optimum and reduce the impact of maximum of iteration, a mutated firefly algorithm is proposed in this paper. The proposed algorithm is based on monitoring the movement of fireflies by using different probability for each firefly and then perform mutation on each firefly according to its probability. Simulations are performed to show the performance of proposed algorithm with standard firefly algorithm, based on ten standard benchmark functions. The results reveals that proposed algorithm improves the convergence speed, accurateness and prevent the premature convergence.

An Effective Hybrid Butterfly Optimization Algorithm with Artificial Bee Colony for Numerical Optimization

In this paper, a new hybrid optimization algorithm which combines the standard Butterfly Optimization Algorithm (BOA) with Artificial Bee Colony (ABC) algorithm is proposed. The proposed algorithm used the advantages of both the algorithms in order to balance the trade-off between exploration and exploitation. Experiments have been conducted on the proposed algorithm using ten benchmark problems having a broad range of dimensions and diverse complexities. The simulation results demonstrate that the convergence speed and accuracy of the proposed algorithm in finding optimal solutions is significantly better than BOA and ABC.

Chaotic grasshopper optimization algorithm for global optimization

Grasshopper optimization algorithm (GOA) is a new meta-heuristic algorithm inspired by the swarming behavior of grasshoppers. The present study introduces chaos theory into the optimization process of GOA so as to accelerate its global convergence speed. The chaotic maps are employed to balance the exploration and exploitation efficiently and the reduction in repulsion/attraction forces between grasshoppers in the optimization process. The proposed chaotic GOA algorithms are benchmarked on thirteen test functions. The results show that the chaotic maps (especially circle map) are able to significantly boost the performance of GOA.

Nature Inspired Range Based Wireless Sensor Node Localization Algorithms

Localization is one of the most important factors highly desirable for the performance of Wireless Sensor Network (WSN). Localization can be stated as the estimation of the location of the sensor nodes in sensor network. In the applications of WSN, the data gathered at sink node will be meaningless without localization information of the nodes. Due to size and complexity factors of the localization problem, it can be formulated as an optimization problem and thus can be approached with optimization algorithms. In this paper, the nature inspired algorithms are used and analyzed for an optimal estimation of the location of sensor nodes. The performance of the nature inspired algorithms viz. Flower pollination algorithm (FPA), Firefly algorithm (FA), Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) for localization in WSN is analyzed in terms of localization accuracy, number of localized nodes and computing time. The comparative analysis has shown that FPA is more proficient in determining the coordinates of nodes by minimizing the localization error as compared to FA, PSO and GWO.

Chaotic Whale Optimization Algorithm

Abstract The Whale Optimization Algorithm (WOA) is a recently developed meta-heuristic optimization algorithm which is based on the hunting mechanism of humpback whales. Similarly to other meta-heuristic algorithms, the main problem faced by WOA is slow convergence speed. So to enhance the global convergence speed and to get better performance, this paper introduces chaos theory into WOA optimization process. Various chaotic maps are considered in the proposed chaotic WOA (CWOA) methods for tuning the main parameter of WOA which helps in controlling exploration and exploitation. The proposed CWOA methods are benchmarked on twenty well-known test functions. The results prove that the chaotic maps (especially Tent map) are able to improve the performance of WOA.

Firefly Algorithm Hybridized with Flower Pollination Algorithm for Multimodal Functions

The successful evolutionary characteristics of biological systems have motivated the researchers to use various nature-inspired algorithms to solve various real-world problems that are complex in nature. These algorithms have the capability to find optimum solutions faster than conventional algorithms. The proposed algorithm uses two terms, exploration and exploitation, effectively from Firefly Algorithm (FA) and Flower Pollination Algorithm (FPA). The proposed algorithm (FA/FPA) is validated using various standard benchmark functions and further its comparison is done with FA and FPA. The result evaluation of the proposed algorithm compute better performance than FA and FPA on most of the benchmark functions.