Multiobjective trajectory planner for industrial robots with payload constraints

This paper presents a novel general method for computing optimal motions of an industrial robot manipulator (AdeptOne XL robot) in the presence of fixed and oscillating obstacles. The optimization model considers the nonlinear manipulator dynamics, actuator constraints, joint limits, and obstacle avoidance. The problem has 6 objective functions, 88 variables, and 21 constraints. Two evolutionary algorithms, namely, elitist non-dominated sorting genetic algorithm (NSGA-II) and multi-objective differential evolution (MODE), have been used for the optimization. Two methods (normalized weighting objective functions and average fitness factor) are used to select the best solution tradeoffs. Two multi-objective performance measures, namely solution spread measure and ratio of non-dominated individuals, are used to evaluate the Pareto optimal fronts. Two multi-objective performance measures, namely, optimizer overhead and algorithm effort, are used to find the computational effort of the optimization algorithm. The trajectories are defined by B-spline functions. The results obtained from NSGA-II and MODE are compared and analyzed.

Simultaneous optimization of two or more objectives is an instance of multi-objective optimization (MOO). However, for most of the Multi-Objective Problems (MOPs), no single solution can optimize all the objective functions simultaneously. Evolutionary algorithms are a type of optimization algorithm used for constructing a well-distributed optimal front more quickly than a much more efficient approach. One of the most commonly used algorithms in this regard is differential evolution (DE). To solve the problem of non-dominated sorting for Multi-Objective DE (MODE), an approach is proposed that reduces the time complexity. DE is commonly recognized as one of the best methods for solving the MOPs. Several versions of DEs have been proposed for MOPs. This article proposes a novel mutation technique, named Environmental Parameter-based Multi-Objective Differential Evolution (EP-MODE) that acquires two additional environmental parameters to preserve diversity and accelerate the convergence. The proposed approach also reduces the time complexity of non-dominated sorting for Multi-Objective DE (MODE). The proposed mutation's performance has been evaluated on DTLZ series and ZDT series benchmark test functions on the Pareto-optimal front and compared with existing multi-objective algorithms (MODE and MODE-RMO). The solution comparatively verifies that the proposed EP-MODE outperforms most of the MODEs for lower dimension functions and higher functions. HIGHLIGHTS New environmental parameters-based mutation has been introduced in a differential evolution algorithm to solve multi-objective optimization problems (EP-mode) The addition of two additional environmental parameters to EP-mode has been found to be appropriate for maintaining diversity while also accelerating the convergence rate EP-MODE evaluated on bi-objective (DTLZ series) and tri-objective (ZDT series) benchmark test functions on the Pareto-optimal front and found EP-MODE's efficiency is higher than that of the MODE algorithm and the MODE algorithm with a ranking-based mutation operator GRAPHICAL ABSTRACT

This paper proposes two multi-objective trajectory planning optimization algorithms, namely, Multi-objective differential evolution (MODE) and Elitist Non-dominated sorting genetic algorithm (NSGA-II). They are applied for a differential drive wheels mobile robot (DDWMR). A cubic NURBS curve is utilized to constitute the mobile robot’s path. The objective functions considered are travel time, traveled length, and actuators efforts. All objective functions are to be minimized. The constraints considered are mobile robot’s kinematic limits, obstacle avoidance, and dynamic limits. Two stationary and five moving obstacles are present around the robot. Numerical simulation results are examined and compared.

Environmental/economic power dispatch problem using multi-objective differential evolution algorithm

Naphtha pyrolysis is one of the important routes for simultaneous production of ethylene and propylene. With recent increase in demand of both ethylene and propylene, understanding of naphtha pyrolysis becomes important for producing with increasing yield of these valuable products. Simultaneous maximization of yield of these two products is mathematically formulated as a multiobjective optimization (MOO) problem. Improved selected scheme is incorporated in the existing multiobjective differential evolution (MODE) algorithm and a new evolutionary algorithm is proposed. Both the evolutionary algorithms [i.e., MODE III and MODE-III with improved selection scheme (MODE III-ISS)] are used for MOO of industrial naphtha cracker unit. Two objectives (maximization of ethylene yield and propylene yield) and decision variables [pressure of the reactor tube (P), temperature of the reactor (T), initial flow rate of naphtha (F 0), and steam to naphtha ratio (SOR)] are considered for MOO study. MODE III and MODE III-ISS algorithms results are compared and presented, which clearly shows that the proposed MODE III-ISS algorithm possesses certain advantages over the MODE III algorithm (such as number of successful selections and percentage convergence with respect to initial number of population points, the quality of the obtained nondominated [ND] solutions).

Parameter estimation of a pressure swing adsorption model for air separation using multi-objective optimisation and support vector regression model

This paper presents an application of the Multiobjective Differential Evolution (MoDE) algorithm for the optimal operation of a complex multipurpose reservoir system. The developed algorithm (MoDE) is compared with the Genetic Algorithm (NSGA-II) using a set of common test problems and the case study. The case study includes part of a complex water supply system located in southwest Brazil that provides water for approximately 20 million people in the Sao Paulo metropolitan area (SPMA). The objectives of the case study include minimization of demand shortage (the difference between demand for water and available water supply), maximization of water quality (or minimization of the deviation from the water quality standards), and minimization of pumping cost. MoDE is applied to the case study using two inflow scenarios: (1) a drought period with inflows below historical average; and (2) a wet period with inflows above historical average. Multiobjective analysis is done by comparing two pairs of objective functions: minimization of demand shortage versus minimization of pumping cost and minimization of demand shortage versus minimization of the deviation from water quality standards. The constraints for the analysis are reservoir capacity, tunnels and channel limitations, and minimum downstream flow for all reservoirs. The proposed MoDE algorithm is outperforming NSGA-II as it converges closer to and provides better spread coverage of the true Pareto front.

MODENAR: Multi-objective differential evolution algorithm for mining numeric association rules

The combination optimization of the train operation plan is an ongoing challenge: while computing power has improved, it is difficult to obtain a complete train operation plan system. With the aim of generating system-optimal operation strategies, a new collaborative optimization method is proposed for line planning problem. Through a set of constraints, the problem is formulated as a two-objective model with the objectives of economic benefits and market effects. An optimization approach with adaptive improvement of control parameters based on the multi-objective differential evolution (MODE) algorithm is proposed to solve the model, and a heuristic algorithm is designed to get a better initial solution. Finally, computational results on benchmark multi-objective problems show that the improvements of the strategies are positive and the optimization result of the improved algorithm has better stability. Meanwhile, based on a numerical example of a practical case study involving a 397-kilometer railway corridor to demonstrate the effectiveness of the proposed model and solution. As the basis of successive decisions, this method can adjust the number of trains according to the passenger flow demand, which greatly saves operating costs.

Particle swarm optimization (PSO) and differential evolution (DE) are meta-heuristics which have been found to be successful in a wide variety of optimization tasks. The high speed of convergence and the relative simplicity of PSO make it a highly viable candidate to be used in multi-objective optimization problems (MOPs). Therefore, several PSO approaches capable to handle MOPs (MOPSOs) have appeared in the past. There are some problems, however, where PSO-based algorithms have shown a premature convergence. On the other hand, multi- objective DEs (MODE) have shown lower speed of convergence than MOPSOs but they have been successfully used in problems where MOPSO have mistakenly converged. In this work, we have developed experiments to observe the convergence behavior, the online convergence, and the diversity of solutions of both meta-heuristics in order to have a better understanding about how particles and solutions move in the search space. To this end, MOPSO and MODE algorithms under (to our best effort) similar conditions were used. Moreover, the ZDT test suite was used on all experiments since it allows to observe Pareto fronts in two-dimensional scatter plots (more details on this are presented on the experiments section). Based on the observations found, modifications to two PSO-based algorithms from the state of the art were proposed resulting in a rise on their performance. It is concluded that MOPSO presents a poor distributed scheme that leads to a more aggressive search. This aggressiveness showed to be detrimental for the selected problems. On the other hand, MODE seemed to generate better distributed points on both decision and objective space allowing it to produce better results.

Mapping of time-consuming multitask applications on a cloud system by multiobjective Differential Evolution

This paper proposes new method based on evolutionary algorithm for optimal trajectory planning of robot manipulator (PUMA 560). The intelligent method is elitist non dominated sorting based genetic algorithm (NSGA-II). This method is superior as it considers multiple criterion to be optimised simultaneously. The multicriterion cost function has to minimize with defined constraints. The problem has 5 objective functions, 32 constraints and 252 variables. Pareto optimal is set of compromise solutions instead of single optimal solution. Pareto optimal fronts are evaluated by the solution spread measure and ratio of non dominated individuals. The B-spline function is used to define robot trajectories.

Weld quality is a critical issue in fabrication industries where products are custom-designed. Multi-objective optimization results number of solutions in the pareto-optimal front. Mathematical regression model based optimization methods are often found to be inadequate for highly non-linear arc welding processes. Thus, various global evolutionary approaches like artificial neural network, genetic algorithm (GA) have been developed. The present work attempts with elitist non-dominated sorting GA (NSGA-II) for optimization of pulsed gas metal arc welding process using back propagation neural network (BPNN) based weld quality feature models. The primary objective to maintain butt joint weld quality is the maximization of tensile strength with minimum plate distortion. BPNN has been used to compute the fitness of each solution after adequate training, whereas NSGA-II algorithm generates the optimum solutions for two conflicting objectives. Welding experiments have been conducted on low carbon steel using response surface methodology. The pareto-optimal front with three ranked solutions after 20th generations was considered as the best without further improvement. The joint strength as well as transverse shrinkage was found to be drastically improved over the design of experimental results as per validated pareto-optimal solutions obtained.

Delivering ready-mixed concrete (RMC) efficiently to construction sites is a practical concern and one of the most challenging tasks for RMC batch managers. Batch plant managers must consider both time and order factors in order to set an RMC truck dispatch schedule that successfully balances batch plant (supplier) and construction site (customer) priorities. This paper develops an optimization framework that integrates discrete event simulation (DES) and multiobjective differential evolution (MODE) to determine the solutions for RMC truck dispatch scheduling. The model takes into consideration uncertainties as well as unexpected situations such as truck breakdowns during delivery. In addition, the chaotic initialized opposition multiobjective differential evolution (COMODE) algorithm is used to optimize the dispatching schedule, which minimizes the total waiting duration both of RMC trucks at construction sites and of construction sites for trucks. A batch plant case study is used to illustrate t...

Due to climatic changes, the probability of occurrence and the intensity of extreme weather events increase day by day which leads to power outages. The probability of failure events is calculated using Monte Carlo simulation technique which is used to identify the most frequent failure events. The least eigenvalues of the inverse reduced Jacobian matrix decide the sensitive buses for load shedding using V-Q sensitivity analysis. This work presents the multi-objective differential evolution (MODE) algorithm for solving resilient constrained optimal power flow (RCOPF) in a deregulated power system. The control variables namely the real power of generators in a base case and under event, generator bus voltage magnitude, the real and reactive power demand of the selected load buses, tap settings and capacitor bank ratings. This algorithm is validated in IEEE 30 bus test system and the Indian 62 bus utility test system to show the effectiveness in four different cases including tie lines and distributed generation.