This paper proposes an efficient algorithm for tracking multiple targets using a network of static and mobile sensors (robots). Multi-target tracking has a broad array of applications, including crowd monitoring, vehicle tracking, warehouse automation, and pedestrian safety, among others. The problem of distributed labeled multi-target tracking comprises constraints on sensing range, communication, label consistency, and motion. Hence, our algorithm strives to minimize label mismatching, communication, movement of robots, and tracking error, which are serious concerns in the existing solutions. The problem is decomposed into two sub-problems: distributed estimation and adaptive movement control. We present a novel track consensus algorithm for estimating the number and tracks of targets, complemented by an efficient label consensus method. This algorithm can effectively identify similar tracks and fuse them in cluttered scenarios. Various movement control strategies are proposed to minimize the moving distance of the robots while keeping the maximum number of targets in the sensing range. The maximum target sensing problem is NP-hard; therefore, we propose and compare heuristic, approximation, and randomized algorithms. We have also verified our proposed solution through extensive simulations and compared the distributed estimation and movement control algorithms with other prominent solutions. We also analyze estimation accuracy using the Optimal Sub-Pattern Assignment (OSPA) metric, asymptotic performance, and communication cost and confirm the real-time computation of our proposed algorithms.

Abstract Recently, many computational intelligence algorithms have been proposed to address software remodularization problem. Unfortunately, it has been observed that the performance of optimizers degrades with the optimization problem containing more than three objectives. In this paper, we propose a many‐objective discrete harmony search (MaDHS) to address the software remodularization problem having more than three objectives. The basic idea of MaDHS is that it uses the quality indicator I ϵ + and external archive to rank and store the nondominated solutions. Along with MaDHS, five remodularization objectives, ie, low coupling, high cohesion, low modification degree, quality of class distribution, and low package instability have also been adapted to improve the package structure of existing object‐oriented software systems. To improve the accuracy of modularization solution, the coupling and cohesion objectives are formulated in terms of various dimensions of direct coupling relationships. To test the supremacy of the proposed approach, it is evaluated over eight real‐world object‐oriented software systems. Simulation results show that the proposed approach outperforms the other existing approaches in terms of couplings, cohesion, modularization quality, modularization merit factor, rate per refactoring of achieved improvement, and external developers view.

This article presents a novel variance-based harmony search algorithm (VHS) for solving optimization problems. VHS incorporates the concepts borrowed from the invasive weed optimization technique to improve the performance of the harmony search algorithm (HS). This eliminates the main problem of constant parameter setting in the algorithm proposed recently and named as explorative HS. It uses the variance of a current population as well as presents a solution vector to improvise the harmony memory. In addition, the dynamic pitch adjustment operator is used to avoid solution oscillation. The proposed algorithm is evaluated on 14 standard benchmark functions of various characteristics. The performance of the proposed algorithm is investigated and compared with classical HS, an improved version of HS, the global best HS, self-adaptive HS, explorative HS, and the recently proposed state-of-art gravitational search algorithm. Experimental results reveal that the proposed algorithm outperforms the above-mentioned approaches. The effects of scalability, noise, harmony memory size, and harmony memory consideration rate have also been investigated with the proposed algorithm. The proposed algorithm is then employed for a data clustering problem. Four real-life datasets selected from the UCI machine learning repository have been used. The results indicate that the VHS-based clustering outperforms the existing well-known clustering algorithms.