Stefan Ivić

Stefan Ivić, Siniša Družeta, Luka Grbčić

Benchmarking optimization algorithms is fundamental for the advancement of computational intelligence. However, widely adopted artificial test suites exhibit limited correspondence with the diversity and complexity of real-world engineering optimization tasks. This paper presents a new benchmark suite comprising 235 bounded, continuous, unconstrained optimization problems, the majority derived from engineering design and simulation scenarios, including computational fluid dynamics and finite element analysis models. In conjunction with this suite, a novel performance metric is introduced, which employs random sampling as a statistical reference, providing nonlinear normalization of objective values and enabling unbiased comparison of algorithmic efficiency across heterogeneous problems. Using this framework, 20 deterministic and stochastic optimization methods were systematically evaluated through hundreds of independent runs per problem, ensuring statistical robustness. The results indicate that only a few of the tested optimization methods consistently achieve excellent performance, while several commonly used metaheuristics exhibit severe efficiency loss on engineering-type problems, emphasizing the limitations of conventional benchmarks. Furthermore, the conducted tests are used for analyzing various features of the optimization methods, providing practical guidelines for their application. The proposed test suite and metric together offer a transparent, reproducible, and practically relevant platform for evaluating and comparing optimization methods, thereby narrowing the gap between the available benchmark tests and realistic engineering applications.

Stella Dumenčić, Luka Lanča, Karlo Jakac et al.

Search and rescue (SAR) missions require reliable search methods to locate survivors, especially in challenging or inaccessible environments. This is why introducing unmanned aerial vehicles (UAVs) can be of great help to enhance the efficiency of SAR missions while simultaneously increasing the safety of everyone involved in the mission. Motivated by this, we design and experiment with autonomous UAV search for humans in a Mediterranean karst environment. The UAVs are directed using Heat equation-driven area coverage (HEDAC) ergodic control method according to known probability density and detection function. The implemented sensing framework consists of a probabilistic search model, motion control system, and computer vision object detection. It enables calculation of the probability of the target being detected in the SAR mission, and this paper focuses on experimental validation of proposed probabilistic framework and UAV control. The uniform probability density to ensure the even probability of finding the targets in the desired search area is achieved by assigning suitably thought-out tasks to 78 volunteers. The detection model is based on YOLO and trained with a previously collected ortho-photo image database. The experimental search is carefully planned and conducted, while as many parameters as possible are recorded. The thorough analysis consists of the motion control system, object detection, and the search validation. The assessment of the detection and search performance provides strong indication that the designed detection model in the UAV control algorithm is aligned with real-world results.

Stefan Ivić, Ante Sikirica, Bojan Crnković

Heat Equation Driven Area Coverage (HEDAC) is a state-of-the-art multi-agent ergodic motion control guided by a gradient of a potential field. A finite element method is hereby implemented to obtain a solution of the Helmholtz partial differential equation, which models the potential field for surveying motion control. This allows us to survey arbitrarily shaped domains and to include obstacles in an elegant and robust manner intrinsic to HEDAC's fundamental idea. For a simple kinematic motion, the obstacles and boundary avoidance constraints are successfully handled by directing the agent motion with the gradient of the potential. However, including additional constraints, such as the minimal clearance distance from stationary and moving obstacles and the minimal path curvature radius, requires further alternations of the control algorithm. We introduce a relatively simple yet robust approach for handling these constraints by formulating a straightforward optimization problem based on collision-free escape route maneuvers. This approach provides a guaranteed collision avoidance mechanism while being computationally inexpensive as a result of the optimization problem partitioning. The proposed motion control is evaluated in three realistic surveying scenarios simulations, showing the effectiveness of the surveying and the robustness of the control algorithm. Furthermore, potential maneuvering difficulties due to improperly defined surveying scenarios are highlighted and we provide guidelines on how to overpass them. The results are promising and indicate real-world applicability of the proposed constrained multi-agent motion control for autonomous surveying and potentially other HEDAC utilizations.

Siniša Družeta, Stefan Ivić

Throughout the course of the development of Particle Swarm Optimization, particle inertia has been established as an important aspect of the method for researching possible method improvements. As a continuation of our previous research, we propose a novel generalized technique of inertia weight adaptation based on individual particle's fitness improvement, called anakatabatic inertia. This technique allows for adapting inertia weight value for each particle corresponding to the particle's increasing or decreasing fitness, i.e. conditioned by particle's ascending (anabatic) or descending (katabatic) movement. The proposed inertia weight control framework was metaoptimized and tested on the 30 test functions of the CEC 2014 test suite. The conducted procedure produced four anakatabatic models, two for each of the PSO methods used (Standard PSO and TVAC-PSO). The benchmark testing results show that using the proposed anakatabatic inertia models reliably yield moderate improvements in accuracy of Standard PSO (final fitness minimum reduced up to 0.09 orders of magnitude) and rather strong improvements for TVAC-PSO (final fitness minimum reduced up to 0.59 orders of magnitude), mostly without any adverse effects on the method's performance.

Stefan Ivić, Bojan Crnković, Hassan Arbabi et al.

Search and detection of objects on the ocean surface is a challenging task due to the complexity of the drift dynamics and lack of known optimal solutions for the path of the search agents. This challenge was highlighted by the unsuccessful search for Malaysian Flight 370 (MH370) which disappeared on March 8, 2014. In this paper, we propose an improvement of a search algorithm rooted in the ergodic theory of dynamical systems which can accommodate complex geometries and uncertainties of the drifting search areas on the ocean surface. We illustrate the effectiveness of this algorithm in a computational replication of the conducted search for MH370. In comparison to conventional search methods, the proposed algorithm leads to an order of magnitude improvement in success rate over the time period of the actual search operation. Simulations of the proposed search control also indicate that the initial success rate of finding debris increases in the event of delayed search commencement. This is due to the existence of convergence zones in the search area which leads to local aggregation of debris in those zones and hence reduction of the effective size of the area to be searched.

Stefan Ivić

Using multiple mobile robots in search missions offers a lot of benefits, but one needs a suitable and competent motion control algorithm which is able to consider sensors characteristics, the uncertainty of target detection and complexity of needed maneuvers in order to make a multi-agent search autonomous. This paper provides a methodology for an autonomous two-dimensional search using multiple unmanned search agents. The proposed methodology relies on an accurate calculation of target occurrence probability distribution based on the initial estimated target distribution and continuous action of spatial variant search agent sensors. The core of the autonomous search process is a high-level motion control for multiple search agents which utilizes the probabilistic model of target occurrence via Heat Equation Driven Area Coverage (HEDAC) method. This centralized motion control algorithm is tailored for handling a group of search agents which are heterogeneous in both motion and sensing characteristics. The motion of agents is directed by the gradient of the potential field which provides near-ergodic exploration of the search space. The proposed method is tested on three realistic search mission simulations and compared with three alternative methods, where HEDAC outperforms all alternatives in all tests. Conventional search strategies need about double the time to achieve proportionate detection rate when compared to HEDAC controlled search. The scalability test showed that increasing the number of HEDAC controlled search agents, although somewhat deteriorating the search efficiency, provides needed speed-up of the search. This study shows the flexibility and competence of the proposed method and gives a strong foundation for possible real-world applications.

Siniša Družeta, Stefan Ivić, Luka Grbčić et al.

Previous research showed that conditioning a PSO agent's movement based on its personal fitness improvement enhances the standard PSO method. In this article, languid particle dynamics (LPD) technique is used on five adequate and widely used PSO variants. Five unmodified PSO variants were tested against their LPD-implemented counterparts on three search space dimensionalities (10, 20, and 50 dimensions) and 30 test functions of the CEC 2014 benchmark test. In the preliminary phase of the testing four of the five tested PSO variants showed improvement in accuracy. The worst and best-achieving variants from preliminary test went through detailed investigation on 220 and 770 combinations of method parameters, where both variants showed overall gains in accuracy when enhanced with LPD. Finally, the results obtained with best achieving PSO parameters were subject to statistical analysis which showed that the two variants give statistically significant improvements in accuracy for 13-50% of the test functions.