Nebojsa Bacanin

Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Optimization Problem with Entropy Diversity Constraint

Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.

Improved seeker optimization algorithm hybridized with firefly algorithm for constrained optimization problems

Seeker optimization algorithm is one of the recent swarm intelligence metaheuristics for hard optimization problems. It is based on the human group search behavior and it was successfully applied to various numerical optimization problems. While the seeker optimization algorithm was proven to be successful for different specific problems, it was not properly tested on a wide set of benchmark functions. Our testing on the standard well-known set of benchmark functions shows that the seeker optimization algorithm has serious problems with some types of functions. In this paper we introduced modifications to the seeker optimization algorithm to control exploitation/exploration balance and hybridized it with elements of the firefly algorithm that improved its exploitation capabilities. The firefly algorithm alone also exhibits deficiencies. Our proposed modified and hybridized seeker optimization algorithm not only overcame shortcomings of the original algorithms, but also outperformed other state-of-the-art swarm intelligence algorithms.

Multilevel image thresholding by fireworks algorithm

This paper presents implementation of the recent fireworks algorithm adjusted for solving multilevel image thresholding problem. This is an important problem since it is often used in image processing for the purpose of image segmentation. Since the number of possible threshold combinations grows exponentially with the number of desirable thresholds, standard deterministic methods could not generate satisfying results when tackling this problem. To test the performance of our proposed approach, we employed Kapurs maximum entropy thresholding function on standard benchmark images where the optimal solutions are known (up to five thresholding points) from the exhaustive search. Results show that our approach has great potential in this field.

JPEG quantization tables selection by the firefly algorithm

JPEG is the prevailing compression algorithm used for digital images. Compression ratio and quality depend on quantization tables that are matrixes of 64 integers. The quality of compression for many applications has to be determined not by human judgment, but by software systems that perform some processing on compressed images, based on successfulness of such processing. Since there are many such applications, there is not unique best quantization table but it has to be selected for each application. Quantization table selection is intractable combinatorial problem that can be successfully solved by swarm intelligence metaheuristics. In this paper we present framework for application of the recently introduced firefly algorithm to the quantization table selection problem for different image similarity metrics.

Upgraded Firefly Algorithm for Portfolio Optimization Problem

Portfolio selection is a well-known intractable research problem in the area of economics and finance. There are many definitions of the problem that by introduction of additional constraints try to make it closer to the real-word conditions. Firefly algorithm is one of the latest swarm intelligence metaheuristics that was very successfully applied to both, unconstrained and constrained hard optimization problems. In this paper we adjusted firefly algorithm to the portfolio optimization problem and since the results were not completely satisfactory, we modified it so that better exploitation/exploration balance was achieved. We tested our improved algorithm on unconstrained portfolio problem, as well as on the problem formulation with cardinality and bounding constraints. We used official benchmark data sets from the OR-Library, and included data from Hang Seng in Hong Kong, DAX 100 in Germany and FTSE 100 in UK with 31, 85 and 89 assets respectively. Our upgraded algorithm proved to be uniformly better than the original one. Additionally, we compared it on the same data set to five other optimization metaheuristics from the literature and our upgraded firefly algorithm was better in most cases measured by all performance indicators.

Fireworks algorithm for RFID network planning problem

This paper describes fireworks algorithm adjusted for solving multi-objective radio frequency identification (RFID) network planning problem. RFID network planning is a hard optimization problem that attracts research attention since the usage of the RFID technology expanded in many industries. Recent fireworks algorithm was successfully applied to other hard optimization problems. In our implementation of the fireworks algorithm for multi-objective RFID network planning problem we used hierarchical approach to objectives. For experimental purposes we used standard benchmark sets. A comparative analysis with other state-of-the-art metaheuristics proved that our proposed approach outperformed other algorithms. It was successful in achieving total coverage without interference with smaller number of deployed readers and less transmitted power.

Hybridized bat algorithm for multi-objective radio frequency identification (RFID) network planning

This paper introduces implementation of hybridized bat algorithm for multi-objective radio frequency identification network planning problem. Multi-objective RFID problem is a well known hard optimization problem that can be solved by using swarm intelligence algorithms. Bat algorithm is a recent mataheuristic, proved to be very successful for tackling such tasks. In our implementation, we hybridized bat algorithm with the artificial bee colony algorithm and adapted it for solving radio frequency identification network planning problem. In the experimental section, we have first shown, by using standard bound-constrained benchmark functions, that our hybridization is justified and that it improves results compared to standard bat algorithm, as well as to other state-of-the-art algorithms. After that, we examined performance of our proposed approach on illustrative RFID network planning problem and compared it with other results from the literature where our proposed algorithm proved to be more successful.

Cuckoo Search and Bat Algorithm Applied to Training Feed-Forward Neural Networks

Training of feed-forward neural networks is a well-known and important hard optimization problem, frequently used for classification purpose. Swarm intelligence metaheuristics have been successfully used for such optimization problems. In this chapter we present how cuckoo search and bat algorithm, as well as the modified version of the bat algorithm, were adjusted and applied to the training of feed-forward neural networks. We used these three algorithms to search for the optimal synaptic weights of the neural network in order to minimize the function errors. The testing was done on four well-known benchmark classification problems. Since the number of neurons in hidden layers may strongly influence the performance of artificial neural networks, we considered several neural networks architectures for different number of neurons in the hidden layers. Results show that the performance of the cuckoo search and bat algorithms is comparable to other state-of-the-art nondeterministic optimization algorithms, with some advantage of the cuckoo search. However, modified bat algorithm outperformed all other algorithms which shows great potential of this recent swarm intelligence algorithm.

Firefly algorithm for multi-objective RFID network planning problem

RFID network planning involves many objectives and constraints and it belongs to the class of NP-hard problems. Such problems were recently successfully tackled by nondeterministic optimization metaheuristics where swarm intelligence represents a prominent branch. We present improved firefly algorithm adjusted for multi-objective RFID network planning where our proposed algorithm improved results considering all relevant performance measures tested on the same benchmark functions and compared to the previously known results from the literature.

An algorithm for handwritten digit recognition using projection histograms and SVM classifier

Higher level of image processing usually contains some kind of recognition. Digit recognition is common in applications and handwritten digit recognition is an important subfield. Handwritten digits are characterized by large variations so template matching, in general, is not very efficient. In this paper we describe an algorithm for handwritten digit recognition based on projections histograms. Classification is facilitated by carefully tuned 45 support vector machines (SVM) using One Against One strategy. Our proposed algorithm was tested on standard benchmark images from MNIST database and it achieved remarkable global accuracy of 99.05%, with possibilities for further improvement.