Single Mobile Robot Scheduling Problem: A Survey of Current Biologically Inspired Algorithms, Research Challenges and Real-World Applications

During the most recent years, a lot of research has been done in creating robots with more self-governance so that they can overcome the challenges that real world environments present. The robot's limited versatility in real world applications can be overcome by the development of Legged robots. Also, as they permit movement in unavailable territory to robots with wheels, Legged Robots are more advantageous. But the potency of the legged robots explicitly its energy usage among alternate points of view really fall behind robots that use wheels. So, the present status of development, there are as yet a few perspectives that need to be analysed, optimized and enhanced. This paper presents review of literature of various biologically inspired legged robots, various techniques adopted for their analysis and optimization and the analysis and optimization of the ones that are not biologically inspired

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Advanced Design Optimisation by Means of Multiobjective Evolutionary Algorithms: The Case of Two Real World Applications

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.572.589 ◽

2013 ◽

Vol 572 ◽

pp. 589-592

Author(s):

Elhadj Benkhelifa ◽

Ashutosh Tiwari ◽

Mohamed Abdel-Maguid

Keyword(s):

Real World ◽

Electronic Systems ◽

Multiobjective Evolutionary Algorithms ◽

Biologically Inspired ◽

Design Optimisation ◽

Design Constraints ◽

Micro Electro Mechanical Systems ◽

Conflicting Objectives ◽

Real World Applications ◽

Computational Platform

The Design Optimisation (DO) of Complex Systems is often a multidisciplinary task and involves multiple conflicting objectives and design constraints, where conventional methods cannot solve efficiently. This paper presents Advanced DO by Means of Evolutional Algorithms in two Real World Applications Electronics and Micro-Electro-Mechanical-Systems (MEMS). The former is presented in the context of multi-objective evolutionary synthesis and optimisation of analogue systems. As for the latter, DO of MEMS bio-mimetically is a very novel area of research, Which addresses the compelling change in the traditional landscape of the associated research disciplines by seeking to provide a novel biologically inspired computational platform for DO of micro-scale designs. This paper presents the latest advancements in the application of EAs in the DO of MEMS and analogue electronic systems and the emergence of the new area of Multidisciplinary Optimisation'.

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Cooperative Co-Evolution Algorithm with an MRF-Based Decomposition Strategy for Stochastic Flexible Job Shop Scheduling

Mathematics ◽

10.3390/math7040318 ◽

2019 ◽

Vol 7 (4) ◽

pp. 318 ◽

Cited By ~ 3

Author(s):

Lu Sun ◽

Lin Lin ◽

Haojie Li ◽

Mitsuo Gen

Keyword(s):

Real World ◽

Job Shop ◽

Job Shop Scheduling ◽

Flexible Scheduling ◽

Flexible Job Shop ◽

Scheduling Problem ◽

Flexible Job Shop Scheduling ◽

Shop Scheduling ◽

Real World Applications ◽

Evolution Algorithm

Flexible job shop scheduling is an important issue in the integration of research area and real-world applications. The traditional flexible scheduling problem always assumes that the processing time of each operation is fixed value and given in advance. However, the stochastic factors in the real-world applications cannot be ignored, especially for the processing times. We proposed a hybrid cooperative co-evolution algorithm with a Markov random field (MRF)-based decomposition strategy (hCEA-MRF) for solving the stochastic flexible scheduling problem with the objective to minimize the expectation and variance of makespan. First, an improved cooperative co-evolution algorithm which is good at preserving of evolutionary information is adopted in hCEA-MRF. Second, a MRF-based decomposition strategy is designed for decomposing all decision variables based on the learned network structure and the parameters of MRF. Then, a self-adaptive parameter strategy is adopted to overcome the status where the parameters cannot be accurately estimated when facing the stochastic factors. Finally, numerical experiments demonstrate the effectiveness and efficiency of the proposed algorithm and show the superiority compared with the state-of-the-art from the literature.

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