Nature-Inspired Techniques For Dynamic Constraint Satisfaction Problems

Combinatorial applications such as configuration, transportation and resource allocation, often operate under highly dynamic and unpredictable environments. In this regard, one of the main challenges is to maintain a consistent solution anytime constraints are (dynamically) added. While many solvers have been developed to tackle these applications, they often work under idealized assumptions of environmental stability. In order to address limitation, we propose a methodology, relying on nature-inspired techniques, for solving constraint problems when constraints are added dynamically. The choice for nature-inspired techniques is motivated by the fact that these are iterative algorithms, capable of maintaining a set of promising solutions, at each iteration. Our methodology takes advantage of these two properties, as follows. We first solve the initial constraint problem and save the final state (and the related population) after obtaining a consistent solution. This saved context will then be used as a resume point for finding, in an incremental manner, new solutions to subsequent variants of the problem, anytime new constraints are added. More precisely, once a solution is found, we resume from the current state to search for a new one (if the old solution is no longer feasible), when new constraints are added. This can be seen as an optimization problem where we look for a new feasible solution satisfying old and new constraints, while minimizing the differences with the solution of the previous problem, in sequence. This latter objective ensures to find the least disruptive solution, as this is very important in many applications including scheduling, planning and timetabling. Following on our proposed methodology, we have developed the dynamic variant of several nature-inspired techniques to tackle dynamic constraint problems. Constraint problems are represented using the well-known Constraint Satisfaction Problem (CSP) paradigm. Dealing with constraint additions in a dynamic environment can then be expressed as a series of static CSPs, each resulting from a change in the previous one by adding new constraints. This sequence of CSPs is called the Dynamic CSP (DCSP). To assess the performance of our proposed methodology, we conducted several experiments on randomly generated DCSP instances, following the RB model. The results of the experiments are reported and discussed.

Vessel Scheduling Optimization Model Based on Variable Speed in a Seaport with One-Way Navigation Channel

To improve the efficiency of in-wharf vessels and out-wharf vessels in seaports, taking into account the characteristics of vessel speeds that are not fixed, a vessel scheduling method with whole voyage constraints is proposed. Based on multi-time constraints, the concept of a minimum safety time interval (MSTI) is clarified to make the mathematical formula more compact and easier to understand. Combining the time window concept, a calculation method for the navigable time window constrained by tidal height and drafts for vessels is proposed. In addition, the nonlinear global constraint problem is converted into a linear problem discretely. With the minimum average waiting time as the goal, the genetic algorithm (GA) is designed to optimize the reformulated vessel scheduling problem (VSP). The scheduling methods under different priorities, such as the first-in-first-out principle, the largest-draft-vessel-first-service principle, and the random service principle are compared and analyzed experimentally with the simulation data. The results indicate that the reformulated and simplified VSP model has a smaller relative error compared with the general priority scheduling rules and is versatile, can effectively improve the efficiency of vessel optimization scheduling, and can ensure traffic safety.

Integrated Optimization of Pipe Routing and Clamp Layout for Aeroengine Using Improved MOALO

Pipe routing and clamp layout for aeroengine are NP-hard computational problems and complex engineering design processes. Besides space constraints and engineering rules, there are assembly constraints between pipes and clamps, which usually lead to repeatedly modifications between pipe routing and clamp layout designs. In order to solve the problems of assembly constraints and design coupling between them, an integrated optimization method for pipe routing and clamp layout is proposed. To this end, the MOALO (multiobjective ant lion optimizer) algorithm is modified by introducing the levy flight strategy to improve the global search performance and convergence speed, and it is further used as a basic computation tool. The integrated optimization method takes pipe and clamp as a whole system and then solves the Pareto solution set of pipe-clamp layouts by using improved MOALO, where the pipe path, clamp position, and rotation angle are selected as decision variables and are further optimized. Inspired by engineering experience, a clamp-based pipe path mechanism considering regular nodes is established to deal with assembly constraint problem. The proposed method comprehensively considers engineering rules of pipe routing and clamp layout and realizes the overall layout optimization of pipe-clamp system while guaranteeing the assembly constraints between pipes and clamps. Finally, some numerical computations and routing examples are conducted to demonstrate the feasibility and effectiveness of the proposed method.

Trajectory following control of lower limb exoskeleton robot based on Udwadia–Kalaba theory

The main objective of this article is to solve the trajectory following problem for lower limb exoskeleton robot by using a novel adaptive robust control method. The uncertainties are considered in lower limb exoskeleton robot system which include initial condition offset, joint resistance, structural vibration, and environmental interferences. They are time-varying and have unknown boundaries. We express the trajectory following problem as a servo constraint problem. In contrast to conventional control methods, Udwadia–Kalaba theory does not make any linearization or approximations. Udwadia–Kalaba theory is adopted to derive the closed-form constrained equation of motion and design the proposed control. We also put forward an adaptive law as a performance index whose type is leakage. The proposed control approach ensures the uniform boundedness and uniform ultimate boundedness of the lower limb exoskeleton robot which are demonstrated via the Lyapunov method. Finally, simulation results have shown the tracking effect of the approach presented in this article.

LF-ACO: an effective formation path planning for multi-mobile robot

<abstract> <p>Multi-robot path planning is a hot problem in the field of robotics. Compared with single-robot path planning, complex problems such as obstacle avoidance and mutual collaboration need to be considered. This paper proposes an efficient leader follower-ant colony optimization (LF-ACO) to solve the collaborative path planning problem. Firstly, a new Multi-factor heuristic functor is proposed, the distance factor heuristic function and the smoothing factor heuristic function. This improves the convergence speed of the algorithm and enhances the smoothness of the initial path. The leader-follower structure is reconstructed for the position constraint problem of multi-robots in a grid environment. Then, the pheromone of the leader ant and the follower ants are used in the pheromone update rule of the ACO to improve the search quality of the formation path. To improve the global search capability, a max-min ant strategy is used. Finally, the path is optimized by the turning point optimization algorithm and dynamic cut-point method to improve path quality further. The simulation and experimental results based on MATLAB and ROS show that the proposed method can successfully solve the path planning and formation problem.</p> </abstract>

A Guidance Strategy for Strapdown Seeker considering Minimum Field-of-View Angle Constraint

An off-axis strapdown seeker in missile may lead to a minimum field-of-view (FOV) angle constraint problem. The goal of this paper is to deal with the problem in guidance. Analysis of kinematics proves that on the premise of attacking stationary target, seeker look angle comes to 0 before or at the end time, and seeker will lose target finally. In order to reduce the distance of seeker losing target, a guidance strategy is proposed to sustain minimum FOV angle constraint during flight. The strategy can be applied on guidance laws with independent orders in longitudinal and lateral channels. By means of a certain rolling maneuver, it keeps the target in the seeker’s limited FOV. Moreover, a lateral guidance order compensation is utilized in the strategy to maintain seeker look angle. Simulations and comparisons are conducted to demonstrate the strategy’s effectiveness. Results show that the guidance strategy can sustain minimum FOV angle constraint longer than classical guidance method.

Two Parallel-Machine Scheduling Problems with Function Constraint

The objective of this paper is to minimize both the makespan and the total completion time. Since parallel-machine scheduling which contains the function constraint problem has been a new issue, this paper explored two parallel-machine scheduling problems with function constraint, which refers to the situation that the two machines have a same function but one of the machines has another. We pointed out that the function constraint occurs not only in the manufacturing system but also in the service system. For the makespan problem, we demonstrated that it is NP-hard in the ordinary sense. In addition, we presented a polynomial time heuristic for this problem and have proved its worst-case ratio is not greater than 5/4. Furthermore, we simulated the performance of the algorithm through computational testing. The overall mean percent error of the heuristic is 0.0565%. The results revealed that the proposed algorithm is quite efficient. For the total completion time problem, we have proved that it can be solved in On4 time.