Development of an enhanced scatter search algorithm using discrete chaotic Arnold’s cat map

Solving optimization problems is an ever-growing subject with an enormous number of algorithms. Examples of such algorithms are Scatter Search (SS) and genetic algorithms. Modifying and improving of algorithms can be done by adding diversity and guidance to them. Chaotic maps are quite sensitive to the initial point, which means even a very slight change in the value of the initial point would result in a dramatic change of the sequence produced by the chaotic map Arnold's Cat Map. Arnold's Cat Map is a chaotic map technique that provides long non-repetitive random-like sequences. Chaotic maps play an important role in improving evolutionary optimization algorithms and meta-heuristics by avoiding local optima and speeding up the convergence. This paper proposes an implementation of the scatter search algorithm with travelling salesman as a case study, then implements and compares the developed hyper Scatter Arnold's Cat Map Search (SACMS) method against the traditional Scatter Search Algorithm. SACMS is a hyper Scatter Search Algorithm with Arnold's Cat Map Chaotic Algorithm. Scatter Arnold's Cat Map Search shows promising results by decreasing the number of iterations required by the Scatter Search Algorithm to get an optimal solution(s). Travelling Salesman Problem, which is a popular and well-known optimization example, is implemented in this paper to demonstrate the results of the modified algorithm Scatter Arnold's Cat Map Search (SACMS). Implementation of both algorithms is done with the same parameters: population size, number of cities, maximum number of iterations, reference set size, etc. The results show improvement by the modified algorithm in terms of the number of iterations required by SS with an iteration reduction of 10–46 % and improvements in time to obtain solutions with 65 % time reduction

A Novel Non-Collision Paths Planning Strategy For Multi-Manipulator Cooperate Manufacturing System

Analogy to the definition of Human-robot Interaction (HRI), the case of multiple manipulators with shared workspace, non-simultaneous manufacturing tasks and separate objects is named as multi-manipulator cooperation, which is becoming more widely employed in modern industrial manufacturing system and requiring non-collision path planning as a key issue in terms of safety and efficiency. In this paper, a novel method called Sampling based Position Space Map Search (SbPSMS) method which combines the map search method with the time-sampling based method will be proposed, including a minimum distance prediction method based on PSO-BP neural network for collision detection and two candidate position determination methods for search map establishing of all manipulators. After the specific search map simplification process, the local path fragments during each sampling time interval can be determined via cost function, which will be glued together to generate the final collision-free paths. The simulation results not only show that the PSO-BP hybrid algorithm has more accurate of nearly 2mm than the standard BP neural network in minimum distance prediction, but also demonstrated that our proposal can successfully achieve collision avoidance of dual manipulators system whilst meeting the real-time requirements for multi-manipulator cooperate assembling scenarios. The further satisfactory simulation results of triple manipulators suggesting our algorithm can be extended to applications of multiple manipulators cooperate manufacturing.

Electrophysiological Markers of Visuospatial Attention Recovery after Mild Traumatic Brain Injury

Objective: Attentional problems are amongst the most commonly reported complaints following mild traumatic brain injury (mTBI), including difficulties orienting and disengaging attention, sustaining it over time, and dividing attentional resources across multiple simultaneous demands. The objective of this study was to track, using a single novel electrophysiological task, various components associated with the deployment of visuospatial selective attention. Methods: A paradigm was designed to evoke earlier visual evoked potentials (VEPs), as well as attention-related and visuocognitive ERPs. Data from 36 individuals with mTBI (19 subacute, 17 chronic) and 22 uninjured controls are presented. Postconcussion symptoms (PCS), anxiety (BAI), depression (BDI-II) and visual attention (TEA Map Search, DKEFS Trail Making Test) were also assessed. Results: Earlier VEPs (P1, N1), as well as processes related to visuospatial orientation (N2pc) and encoding in visual short-term memory (SPCN), appear comparable in mTBI and control participants. However, there appears to be a disruption in the spatiotemporal dynamics of attention (N2pc-Ptc, P2) in subacute mTBI, which recovers within six months. This is also reflected in altered neuropsychological performance (information processing speed, attentional shifting). Furthermore, orientation of attention (P3a) and working memory processes (P3b) are also affected and remain as such in the chronic post-mTBI period, in co-occurrence with persisting postconcussion symptomatology. Conclusions: This study adds original findings indicating that such a sensitive and rigorous ERP task implemented at diagnostic and follow-up levels could allow for the identification of subtle but complex brain activation and connectivity deficits that can occur following mTBI.

Tagging Address Queries in Maps Search

Map search is a major vertical in all popular search engines. It also plays an important role in personal assistants on mobile, home or desktop devices. A significant fraction of map search traffic is comprised of “address queries” - queries where either the entire query or some terms in it refer to an address or part of an address (road segment, intersection etc.). Here we demonstrate that correctly understanding and tagging address queries are critical for map search engines to fulfill them. We describe several recurrent sequence architectures for tagging such queries. We compare their performance on two subcategories of address queries - single entity (aka single point) addresses and multi entity (aka multi point) addresses, and finish by providing guidance on the best practices when dealing with each of these subcategories.