Showing papers by "Nebojsa Bacanin published in 2018"

Elephant herding optimization algorithm for wireless sensor network localization problem

Abstract: This paper presents elephant herding optimization algorithm (EHO) adopted for solving localization problems in wireless sensor networks. EHO is a relatively new swarm intelligence metaheuristic that obtains promising results when dealing with NP hard problems. Node localization problem in wireless sensor networks, that belongs to the group of NP hard optimization, represents one of the most significant challenges in this domain. The goal of node localization is to set geographical co-ordinates for each sensor node with unknown position that is randomly deployed in the monitoring area. Node localization is required to report the origin of events, assist group querying of sensors, routing and network coverage. The implementation of the EHO algorithm for node localization problem was not found in the literature. In the experimental section of this paper, we show comparative analysis with other state-of-the-art algorithms tested on the same problem instance.

Mobile Robot Path Planning by Improved Brain Storm Optimization Algorithm

Abstract: Robots have found their purpose in various situations, from speeding the manufacturing processes to performing complicated tasks in dangerous and hostile environments. One of the important problems in robotics is mobile robot path planning. Robot path planning represents a hard optimization problem that needs to be solved in numerous applications. In this paper we propose path planning method in environments with static obstacles based on the recent swarm intelligence algorithm, brain storm optimization. The brain storm optimization algorithm was improved by local search procedure that each new candidate solution moves to the local best position thus reducing computational time. We tested the proposed method on several benchmark examples from the literature and it has been shown that our approach finds better and more consistent paths using less computational time.

Monarch butterfly optimization algorithm for localization in wireless sensor networks

Abstract: In this paper we adjusted recent monarch butterfly optimization algorithm for solving NP-hard wireless sensor networks optimization problem, as one of the most important challenges in this area. The main goal of node localization problem is to find geographical coordinates of each sensor node with unknown position that are randomly deployed in the monitoring area. Monarch butterfly optimization is novel swarm intelligence approach and it was not tested on this or similar problems before. We adjusted basic implementation of the monarch butterfly optimization method and tested it on several problem instances that are found in the literature. We also performed a comparative analysis with other swarm intelligence metaheuristics that were tested on the same problem instances. By examining results of empirical experiments and by comparative analysis of the results from other approaches, we established that butterfly optimization algorithm can be successfully applied to wireless sensor networks optimization problem.

Wireless Sensor Network Localization Problem by Hybridized Moth Search Algorithm

Abstract: Wireless sensor networks are widely used and consequently represent an important research field. The objective of the node localization problem, that belongs to the group of NP-hard tasks, is to find geographical coordinates of each sensor node with unknown position that are randomly deployed in the monitoring area. Such hard optimization problems are successfully solved by the swarm intelligence algorithms. This paper presents hybridized recent swarm intelligence moth search algorithm adapted for solving localization problem in wireless sensor networks. The application of the moth search algorithm for node localization problem was not found in the literature survey. In the experimental section of this paper we show comparative analysis between the original and hybridized moth search algorithm, as well as with other state-of-the-art algorithms that were tested on the same problem instances of node localization problem. According to experimental results, both, basic moth search and hybridized moth search algorithms are promising approaches for dealing with this kind of problem.

Moth Search Algorithm for Drone Placement Problem

Abstract: This paper presents implementation of the moth search algorithm adjusted for solving static drone location problem. The optimal location of drones is one of the most important issues in this domain, and it belongs to the group of NP-hard optimization. The objective of the model applied in this paper is to establish monitoring all targets with the least possible number of drones. For testing purposes, we used problem instance with 30 uniformly distributed targets in the network domain. According to the results of simulations, where moth search algorithm established full coverage of targets, this approach shows potential in dealing with this kind of problem.

Hybridized moth search algorithm for constrained optimization problems

Abstract: In this paper, hybridized moth search algorithm adjusted for tackling constrained optimization problems, is presented. The moth search algorithm is new optimization method. The application of this approach for classic constrained functions was not published in any scientific paper before. By analyzing basic moth search, we noticed some deficiencies. To address these deficiencies, we hybridized the moth search algorithm with artificial bee colony metaheuristics. In order to evaluate robustness, convergence speed and solutions' quality of the hybridized moth search algorithm, we conducted tests on set of 13 classic constrained benchmarks. We performed comparative analysis with the original moth search, and with other algorithms, that proved to be robust and efficient optimization methods. According to the results obtained during experiments, and comparative analysis with other approaches, we came to the conclusion that the hybridized moth search algorithm has potential in dealing with constrained functions optimization.

Bare Bones Fireworks Algorithm for Capacitated p-Median Problem

Abstract: The p-median problem represents a widely applicable problem in different fields such as operational research and supply chain management. Numerous versions of the p-median problem are defined in literature and it has been shown that it belongs to the class of NP-hard problems. In this paper a recent swarm intelligence algorithm, the bare bones fireworks algorithm, which is the latest version of the fireworks algorithm is proposed for solving capacitated p-median problem. The proposed method is tested on benchmark datasets with different values for p. Performance of the proposed method was compared to other methods from literature and it exhibited competitive results with possibility for further improvements.

Modified and Hybridized Monarch Butterfly Algorithms for Multi-Objective Optimization

Abstract: This paper presents two improved versions of the monarch butterfly optimization algorithm adopted for solving multi-objective optimization problems. Monarch butterfly optimization is a relatively new swarm intelligence metaheuristic that proved to be robust and efficient method when dealing with NP hard problems. However, in the original monarch butterfly approach some deficiencies were noticed and we addressed these deficiencies by developing one modified, and one hybridized version of the original monarch butterfly algorithm. In the experimental section of this paper we show comparative analysis between the original, and improved versions of monarch butterfly algorithm. According to experimental results, hybridized monarch butterfly approach outperformed all other metaheuristics included in comparative analysis.

Hybridized Artificial Bee Colony Algorithm for Constrained Portfolio Optimization Problem

Abstract: Portfolio selection problem that deals with the optimal allocation of capital is a well-known hard optimization problem in the domains of economics and finance. Basic version of the problem is multi-objective since it deals with maximization of return with simultaneous minimization of risk. Additional real world constraints, including cardinality, make the problem even harder. Many techniques and heuristics have been applied to this intractable optimization problem, however swarm intelligence algorithms have been implemented only few times for this task, even though they are known to be very successful for that class of problems. In this paper, we hybridized artificial bee colony algorithm with elements inspired by genetic algorithms to obtain better balance between intensification and diversification, especially during late stages, and applied the proposed improved algorithm to the cardinality constrained mean-variance version of the portfolio selection problem. Experimental results on standard benchmark datasets from five stock indexes and comparative analysis with other cutting edge algorithms have shown that our proposed algorithm achieved better results considering all relevant metrics i.e. mean Euclidean distance between standard efficiency frontier and heuristic efficiency frontier from sets of Pareto optimal portfolios obtained by tested algorithms, mean return error and variance of return error.

Bare Bones Fireworks Algorithm for the RFID Network Planning Problem

Abstract: In this paper we present bare bones fireworks algorithm implemented and adjusted for solving radio frequency identification (RFID) network planning problem. Bare bones fireworks algorithm is new and simplified version of the fireworks metaheuristic. This approach for the RFID network planning problem was not implemented before according to the literature survey. RFID network planning problem is a well known hard optimization problem and it poses one of the most fundamental challenges in the process of deployment of the RFID network. We tested bare bones fireworks algorithm on one problem model found in the literature and performed comparative analysis with approaches tested on the same problem formulation. We also performed additional set of experiments where the number of readers is considered as the algorithm's parameter. Results obtained from empirical tests prove the robustness and efficiency of the bare bones fireworks metaheuristic for tackling the RFID network planning problem and categorize this new version of the fireworks algorithm as state-of-the-art method for dealing with NP-hard tasks.

Cooperative clustering algorithm based on brain storm optimization and K-means

Abstract: Data analysis and making prediction models are important tasks in numerous fields such as medicine, economy, marketing and others. Data clustering provides useful information and it is a rather common tool for discovering data properties. K-means is one of the simplest clustering algorithm but its severe flow is getting trapped into local optima, hence it can be improved by introducing global search. In this paper, cooperative algorithm based on the brain storm optimization algorithm and k-means is proposed. Local search in brain storm optimization algorithm used for solving clustering problems is improved by introducing one iteration of the k-means algorithm for each generated solution. The proposed method was compared with five nature inspired clustering algorithms and by the basic brain storm optimization. Brain storm optimization combined with k-means algorithm found better solutions, smaller fitness function values, and also reduced execution time compared to other methods from literature.

Hybridized Monarch Butterfly Algorithm for Global Optimization Problems

Abstract: This paper introduces hybridized monarch butterfly optimization algorithm for solving global optimization problems. Despite of the fact that the monarch butterfly optimization algorithm is relatively new approach, it has already showed great potential when tackling NP-hard optimization tasks. However, by analyzing original monarch butterfly algorithm, we noticed some deficiencies in the butterfly adjusting operator that in early iterations exceedingly directs the search process towards the current best solution. To overcome this deficiency, we incorporated firefly’s algorithm search mechanism into the original monarch optimization approach. We tested our algorithm on six standard global optimization benchamarks, and performed comparative analysis with original monarch butterfly optimization, as well as with other five state-of-the-art metaheuristics. Experimental results are promising.

Modified Monarch Butterfly Optimization Algorithm for RFID Network Planning

Abstract: This paper presents an implementation of the modified monarch butterfly optimization algorithm for solving the RFID network planning problem. This problem is one of the most studied challenges in the RFID domain, and was not tackled with the monarch butterfly optimization metaheuristic before. RFID system consists of tags and readers, where readers access from certain distance the information that is stored in tags. By testing basic monarch butterfly algorithm, we noticed that the algorithm can be improved by enhancing exploitation in the late iterations. We tested our approach on the standard RFID model and data set, and conducted comparative analysis with other approaches. According to the performed experiments, we concluded that the modified monarch butterfly optimization can be successfully applied to the RFID network planning problem.

Modified Moth Search Algorithm for Global Optimization Problems

Abstract: This paper presents modified moth search algorithm for solving global optimization problems. Moth search algorithm is novel swarm intelligence metaheuristics. By analyzing original moth search approach, we noticed some deficiencies in the search process of subpopulation 2. Modified moth search addresses these weaknesses. To prove the robustness of our approach we tested our algorithm on six standard global optimization benchamarks and performed comparative analysis with original moth search, as well as with other five state-of the-art metaheuristics. Testing results show that in average modified moth search outperforms other approaches included in comparative analysis.

Rigid Image Registration by Bare Bones Fireworks Algorithm

Abstract: Image registration is widely used in various applications in astronomy, medicine, robotics, and others which makes it an important research topic. Image registration problem refers to image alignments and it can be defined by linear (rigid) or non-linear transformation. Finding the optimal transformation that aligns two images is a hard optimization problem. In this paper, we propose a recent swarm intelligence algorithm, bare bones fireworks algorithm, for solving rigid image registration problem. The proposed method uses normalized cross-correlation as similarity metrics and it was tested on six benchmark images. Obtained results were compared to other approaches from literature and it has been shown that the proposed technique is better in term of registration accuracy.

On Hybrid RSS/TOA Target Localization in NLOS Environments

Abstract: In this work, target localization problem in adverse indoor environments is addressed, where most (if not all) links are non-line-of-sight (NLOS). Localization accuracy in such environments is highly affected by multipath, which makes the problem very challenging. Hence, in order to enhance the localization accuracy, received signal strength (RSS) and time of arrival (TOA) integrated measurements, are considered here. Nevertheless, the derived joint maximum likelihood (ML) problem is highly non-convex and has no closed-form solution; thus, some approximations are required to solve it. We show that, for small noise power, the ML estimator can be tightly approximated by another (non-convex in general) one, given in a form of a generalized trust region sub-problem (GTRS). Hence, exact solution of the derived estimator can be readily obtained by merely a bisection procedure. The proposed algorithm is compared with the state-of-the-art (SOA) RSS/TOA algorithms, as well as its RSS-only and TOA-only complements. Our simulations validate the effectiveness of the proposed approach, outperforming the SOA algorithms in all considered scenarios, and show the benefit of the measurement fusion.

Case Study of Target Localization Based on Hybrid and Traditional Ranging via RSS and TOA

Abstract: This work addresses the problem of target localization in adverse non-line-of-sight (NLOS) environments by using received signal strength (RSS) and time of arrival (TOA) measurements. It is inspired by a recently published work in which authors discuss about a critical distance below and above which combined RSS-TOA measurements are inferior to RSS-only and TOA-only measurements. Here, we employ a state-of-the-art estimator for the considered target localization problem and study its performance against its counterparts that employ each individual measurement exclusively. It is shown that the hybrid approach is not the best one by default. Also, we propose a heuristic approach to choose the best measurement for each link in order to enhance the performance of the estimator. The new approach implicitly relies on the concept of critical distance, but does not assume certain link parameters as given. Our simulations show that the proposed approach works well, matching or even improving the performance of the best fixed choice in all considered scenarios.