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The purpose of this paper is to present a scan matching simultaneous localization and mapping (SLAM) algorithm based on particle filter to generate the grid map online. It mainly focuses on reducing the memory consumption and alleviating the loop closure problem.

The proposed method alleviates the loop closure problem by improving the accuracy of the robot’s pose. First, two improvements were applied to enhance the accuracy of the hill climbing scan matching. Second, a particle filter was used to maintain the diversity of the robot’s pose and then to supply potential seeds to the hill climbing scan matching to ensure that the best match point was the global optimum. The proposed method reduces the memory consumption by maintaining only a single grid map.

Simulation and experimental results have proved that this method can build a consistent map of a complex environment. Meanwhile, it reduced the memory consumption and alleviates the loop closure problem.

In this paper, a new SLAM algorithm has been proposed. It can reduce the memory consumption and alleviate the loop closure problem without lowering the accuracy of the generated grid map.

Metaheuristic algorithms are classified into two categories namely: single-solution and population-based algorithms. Single-solution algorithms perform local search process by employing a single candidate solution trying to improve this solution in its neighborhood. In contrast, population-based algorithms guide the search process by maintaining multiple solutions located in different points of search space. However, the main drawback of single-solution algorithms is that the global optimum may not reach and it may get stuck in local optimum. On the other hand, population-based algorithms with several starting points that maintain the diversity of the solutions globally in the search space and results are of better exploration during the search process. In this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.

In this method, different starting points in initial step, searching locally in neighborhood of each solution, construct a global search in search space for the single-solution algorithm.

The proposed method was tested based on three single-solution algorithms involving hill-climbing (HC), simulated annealing (SA) and tabu search (TS) algorithms when they were applied on 25 benchmark test functions. The results of the basic version of these algorithms were then compared with the same algorithms integrated with the global search proposed in this paper. The statistical analysis of the results proves outperforming of the proposed method. Finally, 18 benchmark feature selection problems were used to test the algorithms and were compared with recent methods proposed in the literature.

In this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.

The evolution of microcomputer hard‐ and software is very dynamic, and by analogy, the application of a microcomputer system can be seen as an adventurous exploration. This metaphor is taken to the extreme to shape a brief and light overview of the state‐of‐the‐art in the domain of microcomputer applications for online information retrieval from external databanks and from CD‐ROMs, and for the subsequent post‐processing of data downloaded to the microcomputer system.

This chapter is a review and discussion of the experience of becoming an Electric Vehicle (EV) owner, with a focus on the importance of online EV communities on social media platforms in providing informal support to new owners during the transition into EV ownership and use.

Becoming an EV owner represents a significant disruption to drivers’ very established and comfortable driving practices. Electric cars force their owners to re-think long-habitual aspects of the driving experience, including driving behaviour, refuelling (practicalities and etiquette), route planning, and the extent of the car’s ‘sphere of access’.

Because of this disruption, new EV owners regularly encounter challenges, including charging, range, new technology, route planning, etiquette, and more. People often need support to overcome these challenges, and EV owner groups on social media are an important source of such support; new owners can receive advice on a range of issues. This chapter presents data extracted from EV owner social media group posts, analysing the discussions and advice that EV owners offer one another, and exploring the various forms of important support available to new owners/drivers.

This chapter shows how online EV communities are very actively used by EV owners and are of particular importance for new owners. These communities welcome new owners/drivers, offer support and advice, respond to questions, give recommendations, and encourage socialising and a form of group identity/bonding. With EV ownership rapidly increasing in many countries, online EV communities have a very important role to play in helping facilitate the international transition to electric mobility.

The use of torque converters on road vehicles has not progressed in this country as fast as it has in the U.S.A., but it very likely will do so in the next few years. The question of providing suitable oil coolers for the torque converter oil was the subject of a paper presented last month before the Society of Automotive Engineers at Milwaukee by R. P. McDonough of the Harrison Radiator Division, General Motors Corporation. Mr. McDonough said that the oils used for this purpose usually approximate the SAE 10W grade and the maximum oil temperature to be tolerated is around 300°F. The principal points given in Mr. McDonough's paper are as follows.

This paper aims to propose a reliable local search algorithm having steepest descent pivot rule for computationally expensive optimization problems. In particular, an application to the design of Permanent Magnet Synchronous Motor (PMSM) drives is shown.

A surrogate assisted Hooke‐Jeeves algorithm (SAHJA) is proposed. The SAHJA is a local search algorithm with the structure of the Hooke‐Jeeves algorithm, which employs a local surrogate model dynamically constructed during the exploratory move at each step of the optimization process.

Several numerical experiments have been designed. These experiments are carried out both on the simulation model (off‐line) and at the actual plant (on‐line). Moreover, the off‐line experiments have been considered in non‐noisy and noisy cases. The numerical results show that use of the SAHJA leads to a saving in terms of computational cost without requiring any extra hardware components.

The surrogate approach in the design of electric drives is novel. In addition, implementation of the proposed surrogate model allows the algorithm not only to reduce computational cost but also to filter noise caused by the sensors and measurement devices.

When making sourcing decisions, both cost optimization and customer demand fulfillment are equally important for firm competitiveness. The purpose of this paper is to develop a stochastic search technique, hybrid genetic algorithm (HGA), for cost-optimized decision making in wholesaler inventory management in a supply chain network of wholesalers, retailers and suppliers.

This study develops a HGA by using a mixture of greedy-based and randomly generated solutions in the initial population and a local search method (hill climbing) applied to individuals selected for performing crossover before crossover is implemented and to the best individual in the population at the end of HGA as well as gene slice and integration.

The application of the proposed HGA is illustrated by considering multiple scenarios and comparing with the other commonly adopted methods of standard genetic algorithm, simulated annealing and tabu search. The simulation results demonstrate the capability of the proposed approach in producing more effective solutions.

The pragmatic importance of this method is for the inventory management of wholesaler operations and this can be scalable to address real contexts with multiple wholesalers and multiple suppliers with variable lead times.

The proposed stochastic-based search techniques have the capability in producing good-quality optimal or suboptimal solutions for large-scale problems within a reasonable time using ordinary computing resources available in firms.

The purpose of this paper is to implement a Web interface for hybrid intelligent systems. Using the implemented Web interface, this paper evaluates two hybrid intelligent systems based on particle swarm optimization, hill climbing and distributed genetic algorithm to solve the node placement problem in wireless mesh networks (WMNs).

The node placement problem in WMNs is well-known to be a computationally hard problem. Therefore, the authors use intelligent algorithms to solve this problem. The implemented systems are intelligent systems based on meta-heuristics algorithms: Particle Swarm Optimization (PSO), Hill Climbing (HC) and Distributed Genetic Algorithm (DGA). The authors implement two hybrid intelligent systems: WMN-PSODGA and WMN-PSOHC-DGA.

The authors carried out simulations using the implemented Web interface. From the simulations results, it was found that the WMN-PSOHC-DGA system has a better performance compared with the WMN-PSODGA system.

For simulations, the authors considered Normal distribution of mesh clients. In the future, the authors need to consider different client distributions, patterns, number of mesh nodes and communication distance.

In this research work, the authors implemented a Web interface for hybrid intelligent systems. The implemented interface can be extended for other metaheuristic algorithms.

Disassembly takes place in remanufacturing, recycling, and disposal, with a line being the best choice for automation. The disassembly line balancing problem seeks a sequence that is feasible, minimizes the number of workstations, and ensures similar idle times, as well as other end-of-life specific concerns. Finding the optimal balance is computationally intensive due to exponential growth. Combinatorial optimization methods hold promise for providing solutions to the problem, which is proven here to be NP-hard. Stochastic (genetic algorithm) and deterministic (greedy/hill-climbing hybrid heuristic) methods are presented and compared. Numerical results are obtained using a recent electronic product case study.