Bat Algoritm

Nature is the best tutor and its designs and strengths are extremely massive and strange that it gives inspiration to researches to imitate nature to solve hard and complex problems in computer sciences. Bio Inspired computing has come up as a new era in computation covering wide range of applications. This paper gives overview of most predominant and successful classes of bio inspired optimization methods involving evolutionary and swarm based algorithms inspired by natural evolution and collective behavior in animals respectively.

Advances in computer technology, coupled with the intention to utilize the limited resources to its best possible way while conforming to the prescribed objective, has led to a wealth of different optimization approaches in engineering problems. Of particular note is the rise of using evolutionary algorithms in obtaining the optimal solutions in the engineering design problems. This paper sets out to compare the optimization performances of three recently developed evolutionary algorithms, namely, bat algorithm, cuckoo search algorithm, and flower pollination algorithm in constrained engineering optimization. Three infamous constrained real world problems, specifically, spring design, welded beam design, and pressure vessel design, are considered in this study. The promising optimization capabilities of all reviewed evolutionary algorithms are shown in the performance assessment.

Every month at least one new population-based nature-inspired algorithm has been released in literature. Until recently, there were probably more than 200 algorithms of this kind in books, papers and proceedings. Many researchers discuss that this research area is becoming flooded with new algorithms that are in fact the old algorithms in a new disguise. Potentially, such behavior could be leading into the emergence of pseudoscience. In this paper, we try to find some answers to the questions what lead authors to propose and develop the new nature-inspired algorithms and what their benefits in doing so are. We propose ways in which to stop the emergence of new algorithms. In line with this, we have found that proposing the new population-based nature-inspired algorithms is actually similar to the swarm intelligence behavior in nature, where the role of population members is acted by authors of the new algorithm with the goal to publish a paper, thus promoting its algorithm and sprea...

This paper proposes a new hybrid swarm intelligence algorithm that encompasses the feature of three major swarm algorithms. It combines the fast convergence of the Cuckoo Search (CS), the dynamic root change of the Firefly Algorithm (FA), and the continuous position update of the Particle Swarm Optimization (PSO). The Compound Swarm Intelligence Algorithm (CSIA) will be used to solve a set of standard benchmark functions. The research study compares the performance of CSIA with that of CS, FA, and PSO, using the same set of benchmark functions. The comparison aims to test if the performance of CSIA is Competitive to that of the CS, FA, and PSO algorithms denoting the solution results of the benchmark functions.

The presence of large amount of data and need for turning such a huge data into knowledge has made data mining an important field of research. The huge amounts of data growing at a fast rate, makes it impossible for humans to analyse and extract knowledge from such data.For solving real-world optimization problems, optimization tools are required. Many Nature Inspired Algorithms namely Swarm Optimization, Firefly Algorithm, and Cuckoo search etc. have been used for mining. This paper presents a survey of some nature inspired algorithm which have been used for mining.

The influencing factors associated with efficient operation of power systems are minimum fuel cost and losses in transmission line. Optimal Power Dispatch (OPD) problem is treated to minimize instantaneous operating cost, incremental cost, and transmission line losses considering various network operating constraint. Newly developed Nature-inspired optimization algorithms approach are proposed in this analysis with robust parameter selections. The results of most popular Genetic Algorithm (GA) and based on swarm behavior Particle Swam Optimization (PSO) are compared with four Nature-inspired metaheuristic algorithms of Cuckoo Search (CS), Bat Algorithm (BA), Flower Pollination Algorithm (FPA), and Firefly Algorithm (FA). The quadratic cost function of power generation and penalty function to account for inequality constraints on dependent variables are added for solving OPD problem.  A common algorithms evaluation parameters such as population size and generation limit are designate...

The major problem encountered when modeling complex systems with agent-based modeling and simulation techniques is the existence of large parameter spaces. A complex system model cannot be expected to reflect the whole of the real system, but by specifying the most appropriate parameters, the actual system can be represented by the model under certain conditions. When the studies conducted in recent years were reviewed, it has been observed that there are few studies for parameter tuning problem in agent based simulations, and these studies have focused on tuning parameters of a single model. In this study, an approach of parameter tuning is proposed by using metaheuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colonies (ABC), Firefly (FA) algorithms. With this hybrid structured study, the parameter tuning problems of the models in the different fields were solved. The new approach offered was tested in two different models, and ...

The continuous planar facility location problem with the connected region of feasible solutions bounded by arcs is a particular case of the constrained Weber problem. This problem is a continuous optimization problem which has a nonconvex feasible set of constraints. This paper suggests appropriate modifications of four metaheuristic algorithms which are defined with the aim of solving this type of nonconvex optimization problems. Also, a comparison of these algorithms to each other as well as to the heuristic algorithm is presented. The artificial bee colony algorithm, firefly algorithm, and their recently proposed improved versions for constrained optimization are appropriately modified and applied to the case study. The heuristic algorithm based on modified Weiszfeld procedure is also implemented for the purpose of comparison with the metaheuristic approaches. Obtained numerical results show that metaheuristic algorithms can be successfully applied to solve the instances of this ...

A frequent problem in numerical analysis is solving the systems of equations. That problem has generated in time a great interest among mathematicians and computer scientists, as evidenced by the large number of numerical methods developed. Besides the classical numerical methods, in the last years were proposed methods inspired by techniques from artificial intelligence. Hybrid methods have been also proposed along the time [15, 19]. The goal of this study is to make a survey of methods inspired from artificial intelligence for solving systems of equations.

Nature is of course a great and enormous source of inspiration for solving hard and composite problems in the field of computer science, transportation engineering, mechanical engineering, management and so on, since it exhibits exceptionally diverse, dynamic, robust, and complex phenomenon. It always finds the optimal solution to resolve its problem establishing perfect balance among its components. This is the driving force behind bio inspired computing. Nature inspired algorithms are meta heuristics that mimics the nature for solving optimization problems thus opening a new era in computation .For the past decades ,numerous research efforts has been concentrated in this particular area. Still being infantile and the results being very astonishing, broadens the scope and feasibility of Nature Inspired Algorithms (NIAs) exploring new areas of application and more opportunities in computing. This paper highlights the comparative analysis of nature inspired swarm Intelligence based o...

A swarm is a group of a single species in which the members interact with one another and with the immediate environment without a principle for control or the emergence of a global intriguing behavior. Swarm-based metaheuristics, including nature-inspired populace-based methods, have been developed to aid the creation of quick, robust, and low-cost solutions for complex problems. Swarm intelligence was proposed as a computational modeling of swarms and has been successfully applied to numerous optimization tasks since its introduction. A correlation with the fundamental Particle Swarm Optimization (PSO) and PSO modifications demonstrates that hybrid swarm optimization outperforms existing strategies. The downside of hybrid swarm optimization is that it frequently tends to arrive at suboptimal solutions. As such, efforts are being made into combining HSO and other algorithms to arrive at better quality solutions

 Firefly Algorithm is a recently developed meta-heuristic algorithm, which is inspired by the flashing behaviour of Firefly. Initially, Firefly algorithm was used to solve the optimization problems of continuous search domain. Further, many researchers have successfully implemented this algorithm in several discrete optimization problems. Although the firefly algorithm behaves like another meta-heuristic method (i.e. Particle Swarm Optimization particle), however, the firefly is robust than that. Due to the presence of an exponential term in its movement equation, firefly algorithm is capable to search optimum value more efficiently than others. This study is, mainly, focused to show the strength of the firefly algorithm to solve the complex problems and to explore the possible research area on the structural engineering field. This study shows about the robustness of the firefly algorithm on the basis of recently published papers that was used to solve the size, shape and topology ...

Engineering optimization problems can be dominated by inconsistent performance requirements and incompatible specifications that can be difficult to detect when supporting mathematical programming models are formulated. Thus, it often proves advantageous to construct a set of options that provide dissimilar approaches to such problems. These alternatives should satisfy the required performance criteria, but be maximally different from each other in their decision spaces. The method for constructing maximally different sets of solutions is referred to as modelling-to-generate-alternatives (MGA). This paper considers a multicriteria method that can generate sets of maximally different alternatives using any population-based solution algorithm. This MGA approach is both computationally efficient and simultaneously produces the prescribed number of maximally different solution alternatives in a single computational run of the procedure. The computational efficacy of this multicriteria M...

Firefly algorithms belong to modern meta-heuristic algorithms inspired by nature that can be successfully applied to continuous optimization problems. In this paper, we have been applied the firefly algorithm, hybridized with local search heuristic, to combinatorial optimization problems, where we use graph 3-coloring problems as test benchmarks. The results of the proposed memetic firefly algorithm (MFFA) were compared with the results of the Hybrid Evolutionary Algorithm (HEA), Tabucol, and the evolutionary algorithm with SAW method (EA-SAW) by coloring the suite of medium-scaled random graphs (graphs with 500 vertices) generated using the Culberson random graph generator. The results of firefly algorithm were very promising and showed a potential that this algorithm could successfully be applied in near future to the other combinatorial optimization problems as well.

Intensive applications and success of metaheuristics in practice have initiated research on their theoretical analysis. Due to the unknown quality of reported solution(s) and the inherently stochastic nature of metaheuristics, the theoretical analysis of their asymptotic convergence towards a global optimum is mainly conducted by means of probability theory. In this paper, we show that principles developed for the theoretical analysis of Bee Colony Optimization metaheuristic hold for swarm intelligence based metaheuristics: they need to implement learning mechanisms in order to properly adapt the probability rule for modification of a candidate solution. We propose selection schemes that a swarm intelligence based metaheuristic needs to incorporate in order to assure the so-called model convergence.

Nowadays, demand for natural pigments has increased dramatically due to the awareness of the toxicity of some synthetic pigments. Because of the high cost of growth medium for natural pigment production, various studies have been carried out to explore medium which are less costly, such as agricultural waste. This study highlight on the application of firefly algorithm (FA) and bat algorithm (BA) in optimizing yellowish-orange pigment production (flexirubin) from the agricultural waste material. At present, response surface methodology (RSM) is the most preferred statistical method in optimizing pigment production. However, in the last two decades, nature-inspired metaheuristics approach has been used extensively in the fermentation process and have continually improve the efficiency in the optimization problem especially in pigment production. This study compared the analytics studies of RSM, FA and BA in the estimation of fermentation parameters (Lactose, Ltryptophan, and KH2PO4) ...

Bat algorithm (BA) is one of the most recent bioinspired algorithms. It is based on the echolocation behavior of microbats. The standard BA is proposed only for continuous optimization problems. In this paper, a binary bat algorithm has been developed and implemented to solve the graph coloring problem. To show the feasibility and the effectiveness of the algorithms, we have used the DIMACS benchmark, and the obtained results are very encouraging. Keywords-bat algorithm,bio-inspired algorithm, discrete bat algorithm, graph coloring problem, heuristic, discrete optimization problem.

Steel buildings are preferred in residential as well as commercial buildings due to their high strength and ductility particularly in regions where earthquakes mostly happen. In the past, steel buildings were designed by using trial and error strategy or designer experience. However, these strategies were not economical. After improvements in computer technology, many optimization methods have been widely used in structural design problems to obtain economical solutions while satisfying design requirements. Traditional optimization methods are inadequate to find a satisfactory solution to structural optimization problems due to complicated nature and discrete design variables of these problems. Metaheuristic techniques have become efficient tools for structural optimization problems since their emergence. These techniques try to improve the solution by using certain strategies that are generally inspired by natural phenomena. Genetic algorithms, evolutionary strategies, simulating a...

In the last two decades, the field of global optimization has become very active, and, in this regard, many deterministic and stochastic algorithms were developed for solving various optimization problems. Among them, swarm intelligence (SI) is a stochastic algorithm that is more flexible and robust and has had the ability to find an optimum solution for high-dimensional optimization and search problems. SI-based algorithms are mainly inspired by the social behavior of fish schooling or bird flocking. Among the SI-based algorithms, Bat algorithm (BA) is one of the recently developed evolutionary algorithms. It employs an echolocation behavior of microbats by varying pulse rates of emission and loudness to perform their search process. In this paper, a modified Bat algorithm (MBA) is developed. The main focus of the MBA is to further enhance the exploration and exploitation search abilities of the original Bat algorithm. The performance of the modified Bat algorithm (MBA) is examined...

College of Applied Professional Mechanical Studies , Trstenik (Serbia) 2 The Faculty of Mechanical and Civil Engineering in Kraljevo of the University in Kragujevac, Kraljevo (Serbia) This paper proposes the application of biologically -inspired algorithms to determine optimal parameters of metal cutting . The objective functions are operation time, cost per product, and surface roughness. Model of metal cutting is non-linear , constrained problem.As an example of biologically -inspired algorithm for solving optimization problems , in this paper we have applied the Cuckoo search algorithm , and the Firefly algorithm. Contemporary analysis of these two methods , as well as their hybridization and the experimental results show that the use of biologically -inspired algorithms is applicable to parameter problems optimization of metal cutting.