A survey of swarm intelligence for dynamic optimization: algorithms and applications

Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given

Mobile Robot Path Finding using Nature Inspired Algorithms - A Review

 In today’s world, Mobile Robot has been widely used for various purposes across several aspects of life. The environments could be static and dynamic. Path planning for mobile robot is a very important problem in robotics. Path Planning for robot could be referred to the determination of a path; a robot takes in to perform a task given a set of key inputs. To find the best and optimal path from the starting point to the goal point, such that time and distance is reduce, in any given environment avoiding collision with obstacles is an interesting area for research. This research presents a review on the application of nature inspired algorithms in solving the problem of mobile robot path planning such that the robot reaches the target station from source station without collision with obstacles. The future of these nature-inspired algorithms on mobile robot is also discussed

Obstacle Avoidance Scheme Based Elite Opposition Bat Algorithm for Unmanned Ground Vehicles

Unmanned Ground Vehicles (UGVs) are intelligent vehicles that operate in an obstacle environment without an onboard human operator but can be controlled autonomously using an obstacle avoidance system or by a human operator from a remote location. In this research, an obstacle avoidance scheme-based elite opposition bat algorithm (EOBA) for UGVs was developed. The obstacle avoidance system comprises a simulation map, a perception system for obstacle detection, and the implementation of EOBA for generating an optimal collision-free path that led the UGV to the goal location. Three distance thresholds of 0.1 m, 0.2 m, and 0.3 m was used in the obstacle detection stage to determine the optimal distance threshold for obstacle avoidance. The performance of the obstacle avoidance scheme was compared with that of bat algorithm (BA) and particle swarm optimization (PSO) techniques. The simulation results show that the distance threshold of 0.3 m is the optimal threshold for obstacle avoidance provided that the size of the obstacle does not exceed the size of the UGV. The EOBA based scheme when compared with BA and PSO schemes obtained an average percentage reduction of 21.82% in terms of path length and 60% in terms of time taken to reach the target destination. The uniqueness of this approach is that the UGV avoid collision with an obstacle at a distance of 0.3 m from nearby obstacles as against taking three steps backward before avoiding obstacl

Bacterial Memetic Algorithm Trained Fuzzy System-Based Model of Single Weld Bead Geometry

This article presents a fuzzy system-based modeling approach to estimate the weld bead geometry (WBG) from the welding process variables (WPVs) and to achieve a specific weld bead shape. The bacterial memetic algorithm (BMA) is applied to solve these problems in two different roles, as a supervised trainer, and as an optimizer. As a supervised trainer, the BMA is applied to tune two different WBG models. The bead geometry properties (BGP) model follows a traditional approach providing the WBG properties as outputs. The direct profile measurement (DPM) model describes the bead profiles points by a non-linear function realized in the form of fuzzy rules. As an optimizer, the BMA utilizes the developed fuzzy systems to find the solution sets of WPVs to acquire the desired WBG. The best performance is achieved by applying six rules in the BGP model and eleven rules in the DPM model. The results indicate that the normalized root means square error for the validation data set lies in the range of 0:40 - 1:56% for the BGP model and 4:49 - 7:52% for the DPM model. The comparative analysis suggests that the BGP model estimates the BWG in a superior manner when several WPVs are altered. The developed fuzzy systems provide a tool for interpreting the effects of the WPVs. The developed optimizer provides multiple valid set of WPVs to produce the desired WBG, thus supporting the selection of those process variables in applications

A generalized laser simulator algorithm for optimal path planning in constraints environment

Path planning plays a vital role in autonomous mobile robot navigation, and it has thus become one of the most studied areas in robotics. Path planning refers to a robot's search for a collision-free and optimal path from a start point to a predefined goal position in a given environment. This research focuses on developing a novel path planning algorithm, called Generalized Laser Simulator (GLS), to solve the path planning problem of mobile robots in a constrained environment. This approach allows finding the path for a mobile robot while avoiding obstacles, searching for a goal, considering some constraints and finding an optimal path during the robot movement in both known and unknown environments. The feasible path is determined between the start and goal positions by generating a wave of points in all directions towards the goal point with adhering to constraints. A simulation study employing the proposed approach is applied to the grid map settings to determine a collision-free path from the start to goal positions. First, the grid mapping of the robot's workspace environment is constructed, and then the borders of the workspace environment are detected based on the new proposed function. This function guides the robot to move toward the desired goal. Two concepts have been implemented to find the best candidate point to move next: minimum distance to goal and maximum index distance to the boundary, integrated by negative probability to sort out the most preferred point for the robot trajectory determination. In order to construct an optimal collision-free path, an optimization step was included to find out the minimum distance within the candidate points that have been determined by GLS while adhering to particular constraint's rules and avoiding obstacles. The proposed algorithm will switch its working pattern based on the goal minimum and boundary maximum index distances. For static obstacle avoidance, the boundaries of the obstacle(s) are considered borders of the environment. However, the algorithm detects obstacles as a new border in dynamic obstacles once it occurs in front of the GLS waves. The proposed method has been tested in several test environments with different degrees of complexity. Twenty different arbitrary environments are categorized into four: Simple, complex, narrow, and maze, with five test environments in each. The results demonstrated that the proposed method could generate an optimal collision-free path. Moreover, the proposed algorithm result are compared to some common algorithms such as the A* algorithm, Probabilistic Road Map, RRT, Bi-directional RRT, and Laser Simulator algorithm to demonstrate its effectiveness. The suggested algorithm outperforms the competition in terms of improving path cost, smoothness, and search time. A statistical test was used to demonstrate the efficiency of the proposed algorithm over the compared methods. The GLS is 7.8 and 5.5 times faster than A* and LS, respectively, generating a path 1.2 and 1.5 times shorter than A* and LS. The mean value of the path cost achieved by the proposed approach is 4% and 15% lower than PRM and RRT, respectively. The mean path cost generated by the LS algorithm, on the other hand, is 14% higher than that generated by the PRM. Finally, to verify the performance of the developed method for generating a collision-free path, experimental studies were carried out using an existing WMR platform in labs and roads. The experimental work investigates complete autonomous WMR path planning in the lab and road environments using live video streaming. The local maps were built using data from live video streaming s by real-time image processing to detect the segments of the lab and road environments. The image processing includes several operations to apply GLS on the prepared local map. The proposed algorithm generates the path within the prepared local map to find the path between start-to-goal positions to avoid obstacles and adhere to constraints. The experimental test shows that the proposed method can generate the shortest path and best smooth trajectory from start to goal points in comparison with the laser simulator

Swarm Robotics

This study analyzes and designs the Swarm intelligence (SI) that Self-organizing migrating algorithm (SOMA) represents to solve industrial practice as well as academic optimization problems, and applies them to swarm robotics. Specifically, the characteristics of SOMA are clarified, shaping the basis for the analysis of SOMA's strengths and weaknesses for the release of SOMA T3A, SOMA Pareto, and iSOMA, with outstanding performance, confirmed by well-known test suites from IEEE CEC 2013, 2015, 2017, and 2019. Besides, the dynamic path planning problem for swarm robotics is handled by the proposed algorithms considered as a prime instance. The computational and simulation results on Matlab have proven the performance of the novel algorithms as well as the correctness of the obstacle avoidance method for mobile robots and drones. Furthermore, two out of the three proposed versions achieved the tie for 3rd (the same ranking with HyDE-DF) and 5th place in the 100-Digit Challenge at CEC 2019, GECCO 2019, and SEMCCO 2019 competition, something that any other version of SOMA has yet to do. They show promising possibilities that SOMA and SI algorithms offer.Tato práce se zabývá analýzou a vylepšením hejnové inteligence, kterou představuje samoorganizující se migrační algoritmus s možností využití v průmyslové praxi a se zaměřením na hejnovou robotiku. Je analyzován algoritmus SOMA, identifikovány silné a slabé stránky a navrženy nové verze SOMA jako SOMA T3A, SOMA Pareto, iSOMA s vynikajícím výkonem, potvrzeným známými testovacími sadami IEEE CEC 2013, 2015, 2017 a 2019. Tyto verze jsou pak aplikovány na problém s dynamickým plánováním dráhy pro hejnovou robotiku. Výsledky výpočtů a simulace v Matlabu prokázaly výkonnost nových algoritmů a správnost metody umožňující vyhýbání se překážkám u mobilních robotů a dronů. Kromě toho dvě ze tří navržených verzí dosáhly na 3. a 5. místo v soutěži 100-Digit Challenge na CEC 2019, GECCO 2019 a SEMCCO 2019, což je potvrzení navržených inovací. Práce tak demonstruje nejen vylepšení SOMA, ale i slibné možnosti hejnové inteligence.460 - Katedra informatikyvyhově