Multi-Objective Intelligent Industrial Robot Calibration Using Meta-Heuristic Optimization Approaches

A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrained multi-objective optimization problem. The proposed MOPSO approach handles the problem as a multi-objective problem with competing and non-commensurable fuel cost, emission and system loss objectives and has a diversity-preserving mechanism using an external memory (call “repository”) and a geographically-based approach to find widely different Pareto-optimal solutions. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed MOPSO approach were carried out on the standard IEEE 30-bus test system. The results revealed the capabilities of the proposed MOPSO approach to generate well-distributed Pareto-optimal non-dominated solutions of multi-objective economic load dispatch. Comparison with Multi-objective Evolutionary Algorithm (MOEA) showed the superiority of the proposed MOPSO approach and confirmed its potential for solving multi-objective economic load dispatch.

In this paper, a novel model is presented for machines and automated guided vehicles' simultaneous scheduling, which addresses an extension of the blocking job shop scheduling problem, considering the transferring of jobs between different machines using a limited number of multi-mode automated guided vehicles. Since the model is strictly NP-hard, and because objectives contradict each other, a meta-heuristic algorithm called 'multi-objective invasive weeds optimisation algorithm' with a new chromosome structure which guarantees the feasibility of solutions is developed to solve the proposed problem. Two other meta-heuristic algorithms namely 'non-dominated sorting genetic algorithm' and 'multi-objective particle swarm optimisation algorithm' are applied to validate the solutions obtained by the developed multi-objective invasive weeds optimisation algorithm. A certain method was applied to select the algorithm with the best performance. The result of ranking the algorithms indicated that the developed multi-objective invasive weeds optimisation algorithm had the best performance in terms of solving the mentioned problems. [Received: 7 January 2017; Revised: 30 December 2017; Revised: 17 August 2018; Revised: 22 January 2019; Accepted: 26 July 2019]

This paper presents a new multi-objective optimization method, which is inspired from the idea of non-dominated sorting genetic algorithm (NSGA) and genetic quantum algorithm (GQA). The GQA has been tested on well known test beds in single objective optimization and compared with the genetic algorithm (GA) in the lead author’s previous work [22]. This paper aims to apply the idea of GQA to multi-objective optimization (MOO). The developed method is called non-dominated sorting genetic quantum algorithm (NSGQA). The developed method is tested with benchmark problems collected from literature, which have characteristics representing various aspects of a MOO problem. Test results show that NSGQA has better performance on most benchmark problems than currently popular MOO methods such as the NSGA. The integration of GQA with MOO, and the systematic comparison with other MOO methods on benchmark problems, should be of general interest to researchers on MOO and to practitioners using MOO methods in design.

In data center companies, cloud computing can host multiple types of heterogeneous virtual machines (VMs) and provide many features, including flexibility, security, support, and even better maintenance than traditional centers. However, some issues need to be considered, such as the optimization of energy usage, utilization of resources, reduction of time consumption, and optimization of virtual machine placement. Therefore, this paper proposes an alternative multiobjective optimization (MOP) approach that combines the salp swarm and sine-cosine algorithms (MOSSASCA) to determine a suitable solution for virtual machine placement (VMP). The objectives of the proposed MOSSASCA are to maximize mean time before a host shutdown (MTBHS), to reduce power consumption, and to minimize service level agreement violations (SLAVs). The proposed method improves the salp swarm and the sine-cosine algorithms using an MOP technique. The SCA works by using a local search approach to improve the performance of traditional SSA by avoiding trapping in a local optimal solution and by increasing convergence speed. To evaluate the quality of MOSSASCA, we perform a series of experiments using different numbers of VMs and physical machines. The results of MOSSASCA are compared with well-known methods, including the nondominated sorting genetic algorithm (NSGA-II), multiobjective particle swarm optimization (MOPSO), a multiobjective evolutionary algorithm with decomposition (MOEAD), and a multiobjective sine-cosine algorithm (MOSCA). The results reveal that MOSSASCA outperforms the compared methods in terms of solving MOP problems and achieving the three objectives. Compared with the other methods, MOSSASCA exhibits a better ability to reduce power consumption and SLAVs while increasing MTBHS. The main differences in terms of power consumption between the MOSCA, MOPSO, MOEAD, and NSGA-II and the MOSSASCA are 0.53, 1.31, 1.36, and 1.44, respectively. Additionally, the MOSSASCA has higher MTBHS value than MOSCA, MOPSO, MOEAD, and NSGA-II by 362.49, 274.70, 585.73 and 672.94, respectively, and the proposed method has lower SLAV values than MOPSO, MOEAD, and NSGA-II by 0.41, 0.28, and 1.27, respectively.

An Evolutionary Particle Swarm Optimisation (EPSO) approach is presented to improve the performance of PSO algorithm for multi-objective optimisation. The proposed approach incorporates non-dominated sorting, adaptive inertia weight and a special mutation operation into particle swarm optimisation to enhance the exploratory capability of the algorithm and improve the diversity of the Pareto solutions. To deal with multi-objective optimisation problems, we use dominance-based rank to guide the flight of particles. The proposed algorithm has been validated using several well-known benchmark test functions and successfully applied to the multi-objective optimal design of alloy steels, which aims at determining the optimal process parameters and the required weight percentages of the chemical composites in order to obtain the pre-defin1ed mechanical properties of the materials. The results have shown that the algorithm can locate the constrained optimal design with a very good accuracy.

In this paper, a multi-objective particle swarm optimization algorithm based on the relative distance strategy is proposed to realize hydrodynamic coefficient identification, which can improve the accuracy of parameter identification without determining the weight manually. Firstly, single-objective particle swarm optimization (PSO) is used to identify the parameters of the model with a low coupling degree. Then, the parameters of the strongly coupled sway and yaw motion model are regarded as multi-objective, and a set of unbiased Pareto optimal solutions are obtained by the multi-objective particle swarm optimization (MOPSO) algorithm. Then the distance function of the Pareto solution is constructed to determine the parametric solution of the model. Finally, experimental identification is carried out by using the data set collected from the real ship. Experimental results show that the accuracy of model parameters obtained by this identification method is about 91.6%, and there is no need to set the weighting coefficient.

Today, the use of renewable energy is increasing rapidly due to the reduction of pollution and the optimal use of available energy. Microgrids (MGs) are used as systems to control and exploit various resources and loads. Due to the connection of different resources in MGs, control and operation of this system are complex and important. In this paper, the optimal utilization of MGs is presented by considering various parameters including production sources, different loads and energy storage. In this paper for energy management in systems with various MGs, we aim to apply the multi-objective optimization algorithm to obtain the optimal energy management in MG, while simultaneously satisfying pollution and economic objectives. The multi-objective particle swarm optimization (MOPSO) method and non-dominated sorting genetic algorithm (NSGA-II) are used to solve the problem and their results are compared. Various factors are considered in the cost function including operation cost, losses, pollution and other operation characteristics. Using multi-objective optimization functions considering cost functions and various constraints ensures optimal operation of the MG. Simulation results verify various models and effectiveness of the proposed methods. Simulation results show that using NSGA-II for scheduling DG resources gives more optimal results in terms of reducing operation cost and losses such that considering operation cost and pollution cost using NSGA-II reduces operation cost and losses by 10% and 4%, respectively.

Controller design is performed to meet some performance criteria. Some of them are the gain and phase margins, control effort, settling time, overshoot, amongst other indexes. Obtaining a controller or compensator that fits all these criteria (or some of them) for a closed loop control is done in both empirical and analytical procedures. The most of these procedures assumes the plant to be controlled could be modelled as first or second-order system. This paper aims to show a way to find the controller parameters based on the multi-objective optimization with a evolutionary approach. The systems to be controlled must be the type SISO (Single Input Single Output) whose transfer functions may have order higher than second order.

An integrated optimization strategy based on Kriging model and multi-objective particle swarm optimization (PSO) algorithm was constructed. As a new surrogate model technology, Kriging model has better fitting precision for nonlinear problem. The Kriging model was adopted to replace computer aided engineering (CAE) simulation as fitness function of multi-objective PSO algorithm, and the computation cost can be reduced greatly. By introducing multi-objective handling mechanism of crowding distance and mutation operator to multi-objective PSO algorithm, the entire Pareto front can be approximated better. It is shown that the multi-objective optimization strategy can get higher solving accuracy and computation efficiency under small sample.

This paper addresses a multi-objective approach for simultaneous dynamic expansion planning of conventional sub-transmission grid and Regional Virtual Power Plant (RVPP). In such a feed-in tariff electricity market, it will assist RVVP’s stakeholders in deciding whether or not they should invest in new equipment’s installation. A Multi-Objective Particle Swarm Optimization (MOPSO) algorithm as a heuristic optimization method is proposed to eliminate the conventional centralized planning, which has led to competition between Regional Electric Company (REC) and RVPP for power delivery in sub-transmission system. Two objective functions are considered for simultaneous expansion planning of these two systems. The first one takes the minimum cost of the REC, as the sub-transmission grid operator, into account while the other one considers profit maximization for RVPP. To achieve the goals, MOPSO algorithm is employed to find the best expansion of REC and best location and capacity of RVPP’s resources. Having solved the proposed multi-objective optimization problem, a Pareto front is determined to show the trade-off between REC and RVPPs' contributions in joint optimal expansion planning of conventional sub-transmission grid and the RVPP. To demonstrate the applicability and effectiveness of the proposed approach, a realistic sub-transmission system in Guilan Province, Iran is used as a test system, and the results are evaluated accordingly.

Digital twin-based industrial cloud robotics: Framework, control approach and implementation

A method for designing miniaturized frequency selective surface (FSS) using equivalent circuit model (ECM) and multi-objective particle swarm optimization (MOPSO) algorithm is proposed in this paper. In the proposed method, ECM describing the desired frequency response is established, and parameters of the ECM are determined using the MOPSO algorithm. Combining the ECM analysis and the MOPSO algorithm, the requirements of desired frequency response can be converted into the constraints of equivalent circuit parameters, which simplifies the optimization process of FSS design. For verification, a single-layer bandstop-bandpass FSS composed of convoluted elements with the unit cell size of 0.073λ 0 × 0.073λ 0 is designed. Simulation results show that the −15 dB bandwidth is from 2.62 to 2.83 GHz, and the −0.5 dB bandwidth is from 3.84 to 5 GHz. Meanwhile, the resonant frequency is stable at different incident angles (up to 80°) under TE and TM polarization. Finally, the proposed FSS prototype is fabricated and measured. The measured results agree well with the simulated ones.

A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems

The aim of a Huff location problem is to locate facilities on a competitive environment to maximize the market share. The customer behavior is formulated based on a Huff gravity model. This paper considers a social impact along with economic and environmental aspects in order to construct new facilities in a competitive supply chain. This problem is a multi-objective mixed integer non-linear programming (MINLP). To deal with the problem, two multi-objective meta-heuristics (i.e., non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO)) are proposed to cope with this problem. The results verify the superiority of the NSGA-II over MOPSO in terms of some comparison metrics. Additionally, a two-stage algorithm is developed to sort non-dominated solutions generated by the NSGA-II. In the first stage, Pareto-optimal solutions are obtained by the NSGA-II, then in second stage, a ranking method based on TOPSIS is performed to select the best solutions among Pareto-optimal one.

In the last decade, multiobjective Particle Swarm Optimization (MOPSO) has been observed as one of the most powerful optimization algorithms in solving Multiobjective Optimization Problems (MOPs). Nowadays, it is becoming increasingly clear that MOPSO can handle complex MOPs based on the competitive-cooperative framework. The goal of this paper is to provide a comprehensive review of MOPSO from the basic principles to hybrid evolutionary strategies. To offer the readers insights on the prominent developments of MOPSO, the key parameters on the convergence and diversity performance of MOPSO were analyzed to reflect the influence on the searching performance of particles. Then, the main advanced MOPSO methods were discussed, as well as the theoretical analysis of multiobjective optimization performance metrics. Even though some hybrid MOPSO methods show promising multiobjective optimization performance, there is much room left for researchers to improve further, in particular in terms of engineering applications. As a result, further in-depth studies are required. This paper should motivate evolutionary computation researchers to pay more attention to this practical yet challenging area.