Iztok Fister

A comprehensive review of firefly algorithms

Abstract The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as engineering practice. Many problems from various areas have been successfully solved using the firefly algorithm and its variants. In order to use the algorithm to solve diverse problems, the original firefly algorithm needs to be modified or hybridized. This paper carries out a comprehensive review of this living and evolving discipline of Swarm Intelligence, in order to show that the firefly algorithm could be applied to every problem arising in practice. On the other hand, it encourages new researchers and algorithm developers to use this simple and yet very efficient algorithm for problem solving. It often guarantees that the obtained results will meet the expectations.

Modified firefly algorithm using quaternion representation

Abstract Quaternions are a number system, which extends complex numbers. They are especially useful in areas where fast rotation calculations are needed, e.g., programming video games or controllers of spacecraft. This paper proposes to use quaternion for the representation of individuals in firefly algorithm so as to enhance the performance of the firefly algorithm and to avoid any stagnation. The preliminary results of our experiments after optimizing a test-suite consisting of ten standard functions, showed that the proposed firefly algorithms using quaternion’s representation improved the results of the original firefly algorithm.

A review of chaos-based firefly algorithms

The firefly algorithm is a member of the swarm intelligence family of algorithms, which have recently showed impressive performances in solving optimization problems. The firefly algorithm, in particular, is applied for solving continuous and discrete optimization problems. In order to tackle different optimization problems efficiently and fast, many variants of the firefly algorithm have recently been developed. Very promising firefly versions use also chaotic maps in order to improve the randomness when generating new solutions and thereby increasing the diversity of the population. The aim of this review is to present a concise but comprehensive overview of firefly algorithms that are enhanced with chaotic maps, to describe in detail the advantages and pitfalls of the many different chaotic maps, as well as to outline promising avenues and open problems for future research.

A novel hybrid self-adaptive bat algorithm.

Nature-inspired algorithms attract many researchers worldwide for solving the hardest optimization problems. One of the newest members of this extensive family is the bat algorithm. To date, many variants of this algorithm have emerged for solving continuous as well as combinatorial problems. One of the more promising variants, a self-adaptive bat algorithm, has recently been proposed that enables a self-adaptation of its control parameters. In this paper, we have hybridized this algorithm using different DE strategies and applied these as a local search heuristics for improving the current best solution directing the swarm of a solution towards the better regions within a search space. The results of exhaustive experiments were promising and have encouraged us to invest more efforts into developing in this direction.

Memetic artificial bee colony algorithm for large-scale global optimization

Memetic computation (MC) has emerged recently as a new paradigm of efficient algorithms for solving the hardest optimization problems. On the other hand, artificial bees colony (ABC) algorithms demonstrate good performances when solving continuous and combinatorial optimization problems. This study tries to use these technologies under the same roof. As a result, a memetic ABC (MABC) algorithm has been developed that is hybridized with two local search heuristics: the Nelder-Mead algorithm (NMA) and the random walk with direction exploitation (RWDE). The former is attended more towards exploration, while the latter more towards exploitation of the search space. The stochastic adaptation rule was employed in order to control the balancing between exploration and exploitation. This MABC algorithm was applied to a Special suite on Large Scale Continuous Global Optimization at the 2012 IEEE Congress on Evolutionary Computation. The obtained results the MABC are comparable with the results of DECC-G, DECC-G*, and MLCC.

Planning the sports training sessions with the bat algorithm

Planning proper sports training has always been a very challenging task for coaches. In line with this, they need to have almost two special abilities: firstly, to have a lot of earlier experiences with sports training and secondly, to know the capability of their athletes very well. New ways in planning sports training have emerged with development of pervasive and mobile technologies. Recently, a GPS receiver is one of the most useful parts of each standard sports watch that enables athletes to track the duration of their sports activities and analyze them later on digital computers using GPS viewers. Most sports watches are also capable of measuring an athletes heart rate during activities. Both measures represent reliable data sources that can be used for planning the sports trainings by coaches. In this paper, we introduce a novel intelligent planning method for sports training sessions, where the training plans are generated on digital computers using the bat algorithm according to reliable data obtained from sports watches. Real-world experiments showed promising results that encouraged us to proceed with this research also in the future.

Cuckoo Search: A Brief Literature Review

Cuckoo search (CS) was introduced by Xin-She Yang and Suash Deb in 2009, and it has attracted great attention due to its promising efficiency in solving many optimization problems and real-world applications. In the last few years, many papers have been published regarding cuckoo search, and the relevant literature has expanded significantly. This chapter summarizes briefly the majority of the literature about cuckoo search in peer-reviewed journals and conferences found so far. These references can be systematically classified into appropriate categories, which can be used as a basis for further research.

Large scale global optimization using self-adaptive differential evolution algorithm

In this paper we present self-adaptive differential evolution algorithm jDElsgo on large scale global optimization. The experimental results obtained by our algorithm on benchmark functions provided for the CEC 2010 competition and special session on Large Scale Global Optimization are presented. The experiments were performed on 20 benchmark functions with high dimension D = 1000. Obtained results show that our algorithm performs highly competitive in comparison with the DECC-G∗, DECC-G and MLCC algorithms.

A comprehensive review of cuckoo search: variants and hybrids

Cuckoo search (CS) is an efficient swarm-intelligence-based algorithm and significant developments have been made since its introduction in 2009. CS has many advantages due to its simplicity and efficiency in solving highly non-linear optimisation problems with real-world engineering applications. This paper provides a timely review of all the state-of-the-art developments in the last five years, including the discussions of theoretical background and research directions for future development of this powerful algorithm.

Hybrid self-adaptive cuckoo search for global optimization

Abstract Adaptation and hybridization typically improve the performances of original algorithm. This paper proposes a novel hybrid self-adaptive cuckoo search algorithm, which extends the original cuckoo search by adding three features, i.e., a balancing of the exploration search strategies within the cuckoo search algorithm, a self-adaptation of cuckoo search control parameters and a linear population reduction. The algorithm was tested on 30 benchmark functions from the CEC-2014 test suite, giving promising results comparable to the algorithms, like the original differential evolution (DE) and original cuckoo search (CS), some powerful variants of modified cuckoo search (i.e., MOCS, CS-VSF) and self-adaptive differential evolution (i.e., jDE, SaDE), while overcoming the results of a winner of the CEC-2014 competition L-Shade remains a great challenge for the future.