Robust Multi-objective Evolutionary Algorithm Research Articles

Robust multi-objective multi-humanoid robots task allocation based on novel hybrid metaheuristic algorithm

Nowadays humanoid robots have generally made dramatic progress, which can form a coalition replacing human work in dangerous environments such as rescue, defense, exploration, etc. In contrast to other types of robots, humanoid robots’ similarity to human make them more suitable for performing such a wide range of tasks. Rescue applications for robots, especially for humanoid robots are exciting. In rescue operating conditions, tasks’ dependencies, tasks’ repetitive accomplishment requirements, robots’ energy consumption, and total tasks’ accomplishment time are key factors. This paper investigates a practical variant of the Multi-Robots Task Allocation (MRTA) problem for humanoid robots as multi-humanoid robots’ task allocation (MHTA) problem. In order to evaluate relevant aspects of the MHTA problem, we proposed a robust Multi-Objective Multi-Humanoid Robots Task allocation (MO-MHTA) algorithm with four objectives, namely energy consumption, total tasks’ accomplishment time, robot’s idle time and fairness were optimized simultaneously in an evolutionary framework in MO-MHTA, which address for the first time. MO-MHTA exhibits multi-objective properties in real-world applications for humanoid robots in two phases. In the first phase, the tasks are partitioned in a fair manner with a proposed constraint k-medoid (CKM) algorithm. In the second phase, a new non-dominated sorting genetic algorithm with special genetic operators is applied. Evaluations based on extensive experiments on the newly proposed benchmark instances with three robust multi-objective evolutionary algorithms (MOEAs) are applied. The proposed algorithm achieves favorable results in comparison to six other algorithms. Besides, the proposed algorithm can be seen as benchmark algorithms for real-world MO-MHTA instances.

K-Balanced Center Location problem: A new multi-objective facility location problem

Balancing workload among a set of facility centers is one of the practical objectives in location problems. In this paper, we introduce a multi-objective optimization facility location problem which considers two goals: minimizing the maximum distance between each client and its closest center, and maximizing workload balance among the centers. To achieve the second goal, we define two objectives, minimizing the maximum number of clients allocated to each center, and minimizing the difference between the maximum and minimum number of clients allocated to each center. We prove the hardness of finding even one Pareto optimal solution in the resulted multi-objective problem. Also, we propose a simple iterative algorithm based on the Voronoi diagram to solve the problem. We show the efficiency of the proposed algorithm using test problems and compare the results with a robust multi-objective evolutionary algorithm.

Analysis and design of electrical machines with material uncertainties in iron and permanent magnet

PurposeManufacturing processes, such as laminations, may introduce uncertainties in the magnetic properties of materials used in electrical machines. This issue, together with magnetization errors, can cause serious deterioration in the performance of the machines. Hence, stochastic material models are required for the study of the influences of the material uncertainties. The purpose of this paper is to present a methodology to study the impact of magnetization pattern uncertainties in permanent magnet electric machines.Design/methodology/approachThe impacts of material uncertainties on the performances of an interior permanent magnet (IPM) machine were analyzed using two different robustness metrics (worst-case analysis and statistical study). In addition, two different robust design formulations were applied to robust multi-objective machine design problems.FindingsThe computational analyses show that material uncertainties may result in deviations of the machine performances and cause nominal solutions to become non-robust.Originality/valueIn this paper, the authors present stochastic models for the quantification of uncertainties in both ferromagnetic and permanent magnet materials. A robust multi-objective evolutionary algorithm is demonstrated and successfully applied to the robust design optimization of an IPM machine considering manufacturing errors and operational condition changes.

Development of a ship weather routing system

A ship route optimization system aimed at facing the challenges of a demanding maritime trade is presented. The paper describes the complete on-board ship weather routing system highlighting the last updates, which are aimed to give it an operational perspective. Ship responses are modelled for any sea-state condition, taking into account the wave directional spreading. The functionalities of a robust multi-objective evolutionary algorithm are exploited to approximate the most favourable set of solutions, successively ranked according to the ship's requirements. The code modularity allows a great flexibility in the treatment of goals and objectives as well as in the input selection.

Intelligent optimization of renewable resource mixes incorporating the effect of fuel risk, fuel cost and CO2 emission

Power system planning is a capital intensive investment-decision problem. The majority of the conventional planning conducted since the last half a century has been based on the least cost approach, keeping in view the optimization of cost and reliability of power supply. Recently, renewable energy sources have found a niche in power system planning owing to concerns arising from fast depletion of fossil fuels, fuel price volatility as well as global climatic changes. Thus, power system planning is under-going a paradigm shift to incorporate such recent technologies. This paper assesses the impact of renewable sources using the portfolio theory to incorporate the effects of fuel price volatility as well as CO2 emissions. An optimization framework using a robust multi-objective evolutionary algorithm, namely NSGA-II, is developed to obtain Pareto optimal solutions. The performance of the proposed approach is assessed and illustrated using the Indian power system considering real-time design practices. The case study for Indian power system validates the efficacy of the proposed methodology as developing countries are also increasing the investment in green energy to increase awareness about clean energy technologies.

A Novel Hybrid Multi-Objective Population Migration Algorithm

This paper presents a multi-objective co-evolutionary population migration algorithm based on Good Point Set (GPSMCPMA) for multi-objective optimization problems (MOP) in view of the characteristics of MOPs. The algorithm introduces the theory of good point set (GPS) and dynamic mutation operator (DMO) and adopts the entire population co-evolutionary migration, based on the concept of Pareto nondomination and global best experience and guidance. The performance of the algorithm is tested through standard multi-objective functions. The experimental results show that the proposed algorithm performs much better in the convergence, diversity and solution distribution than SPEA2, NSGA-II, MOPSO and MOMASEA. It is a fast and robust multi-objective evolutionary algorithm (MOEA) and is applicable to other MOPs.

Novel frameworks for creating robust multi-objective benchmark problems

Robust optimization deals with considering different types of uncertainties during the optimization process in order to obtain reliable solutions, a critical issue when solving real problems. Multiple objectives are another vital aspect of real problems that should be considered during optimization. In order to benchmark the performance of different meta-heuristics, test problems are essential, as the literature shows. Despite the significant number of studies in developing multi-objective test problems, there is currently neither study on the suitability of the current robust multi-objective benchmark problems, nor standard frameworks to create them. This motivates our attempts to investigate the features of the current robust test problems and propose three novel frameworks to generate various robust multi-objective test problems with alterable parameters. As case studies, Robust Multi-Objective Particle Swarm Optimization (RMOPSO), Robust Non-dominated Sorting Genetic Algorithm (RNSGA-II), Robust Multiobjective Evolutionary Algorithm Based on Decomposition (RMOEA/D), Robust Two Local Best Multi-objective Particle Swarm Optimization (R2LB-MOPSO), and Robust Decomposition-Based Multi-objective Evolutionary Algorithm with an Ensemble of Neighborhood Sizes (RENS-MOEA/D) are benchmarked on the proposed test problems. The results show that the proposed frameworks are able to generate robust multi-objective test problems with different adjustable characteristics and levels of difficulty. In addition, the results show that the test problems generated by the proposed frameworks can provide very challenging test beds for effectively benchmarking the performance of robust meta-heuristics.

Aerodynamic Shape Optimisation of Unmanned Aerial Vehicles using Hierarchical Asynchronous Parallel Evolutionary Algorithms

One of the challenges in Unmanned (Combat) Aerial Vehicles (UCAV) is the improvement of aerodynamic performance to complete diverse missions, increase endurance and lower fuel consumption. Recent advances in design tools, materials, electronics and actuators have opened the door for implementation of transonic flow control technologies to improve aerodynamic efficiency. This paper explores the application of a robust Multi-Objective Evolutionary Algorithm (MOEA) for the design and optimisation of aerofoil sections and wing planform of UAVs and UCAVs. The methodology is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing and asynchronous evaluation. For the design and optimisation of UCAV wing planform shape, an aero-diamond planform shape with a jagged trailing edge is considered like saw tooth. Results obtained from the combination between the approach and the aerodynamic analysis tools show the improvement of the aerodynamic efficiency, a set of shock-free aerofoils and the supercritical aero-diamond wing. Results also indicate that the method is capable to produce non-dominated solutions.