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Aiming at obtaining a high-quality global path for a mobile robot which works in complex environments, a modified particle swarm optimization (PSO) algorithm, named random-disturbance self-adaptive particle swarm optimization (RDSAPSO), is proposed in this paper.

A perturbed global updating mechanism is introduced to the global best position to avoid stagnation in RDSAPSO. Moreover, a new self-adaptive strategy is proposed to fine-tune the three control parameters in RDSAPSO to dynamically adjust the exploration and exploitation capabilities of RDSAPSO. Because the convergence of PSO is paramount and influences the quality of the generated path, this paper also analytically investigates the convergence of RDSAPSO and provides a convergence-guaranteed parameter selection principle for RDSAPSO. Finally, a RDSAPSO-based global path planning (GPP) method is developed, in which the feasibility-based rule is applied to handle the constraint of the problem.

In an attempt to validate the proposed method, it is compared against six state-of-the-art evolutionary methods under three different numerical simulations. The simulation results confirm that the proposed method is highly competitive in terms of the path optimality. Moreover, the computation time of the proposed method is comparable with those of the other compared methods.

Therefore, the proposed method can be considered as a vital alternative in the field of GPP.

The computational drawbacks of existing numerical methods have forced researchers to rely on heuristic algorithms. Heuristic methods are powerful in obtaining the solution of optimization problems. Although they are approximate methods (i.e. their solution are good, but not provably optimal), they do not require the derivatives of the objective function and constraints. Also, they use probabilistic transition rules instead of deterministic rules. The purpose of this paper is to present an improved ant colony optimization (IACO) for constrained engineering design problems.

IACO has the capacity to handle continuous and discrete problems by using sub‐optimization mechanism (SOM). SOM is based on the principles of finite element method working as a search‐space updating technique. Also, SOM can reduce the size of pheromone matrices, decision vectors and the number of evaluations. Though IACO decreases pheromone updating operations as well as optimization time, the probability of finding an optimum solution is not reduced.

Utilizing SOM in the ACO algorithm causes a decrease in the size of the pheromone vectors, size of the decision vector, size of the search space, the number of function evaluations, and finally the required optimization time. SOM performs as a search‐space‐updating rule, and it can exchange discrete‐continuous search domain to each other.

The suitability of using ACO for constrained engineering design problems is presented, and applied to optimal design of different engineering problems.

Two-level support with Level 1 consisting of a set of beams and Level 2 consisting of a tree-like structure is an efficient support structure for extrusion-based additive manufacturing (EBAM). However, the literature for finding a slim two-level support is rare. The purpose of this paper is to design a lightweight two-level support structure for EBAM.

To efficiently solve the problem, the lightweight design problem is split into two subproblems: finding a slim Level 1 support and a slim Level 2 support. To solve these two subproblems, this paper develops three efficient metaheuristic algorithms, i.e. genetic algorithm (GA), genetic programming (GP) and particle swarm optimization (PSO). They are problem-independent and are powerful in global search. For the first subproblem, considering the path direction is a critical factor influencing the layout of Level 1 support, this paper solves it by splitting the overhang region into a set of subregions, and determining the path direction (vertical or horizontal) in each subregion using GA. For the second subproblem, a hybrid of two metaheuristic algorithms is proposed: the GP manipulates the topologies of the tree support, while the PSO optimizes the position of nodes and the diameter of tree branches. In particular, each chromosome is encoded as a single virtual tree for GP to make it easy to manipulate Crossover and Mutation. Furthermore, a local strategy of geometric search is designed to help the hybrid algorithm reach a better result.

Simulation results show that the proposed method is preferred over the existing method: it saves the materials of the two-level support up to 26.34%, the materials of the Level 1 support up to 6.62% and the materials of the Level 2 support up to 37.93%. The proposed local strategy of geometric search can further improve the hybrid algorithm, saving up to 17.88% of Level 2 support materials.

The proposed approach for sliming Level 1 support requires the overhanging region to be a rectilinear polygon and the path direction in a subregion to be vertical or horizontal. This limitation limits the further material savings of the Level 1 support. In future research, the proposed approach can be extended to handle an arbitrary overhang region, each with several choices of path directions.

The details of how to integrate the proposed algorithm into the open-source program CuraEngine 4.13.0 is presented. This is helpful for the designers and manufacturers to practice on their own 3D printers.

The path planning of the overhang is a critical factor influencing the distribution of supporting points and will thus influence the shape of the support structure. Different from existing approaches that use single path directions, the proposed method optimizes the volume of the support structure by planning hybrid paths of the overhangs.

The first order reliability method requires optimization algorithms to find the minimum distance from the origin to the limit state surface in the normal space. The purpose of this paper is to develop an improved version of the new metaheuristic algorithm inspired from echolocation behaviour of bats, namely, the bat algorithm (BA) dedicated to perform structural reliability analysis.

Modifications have been embedded to the standard BA to enhance its efficiency, robustness and reliability. In addition, a new adaptive penalty equation dedicated to solve the problem of the determination of the reliability index and a proposition on the limit state formulation are presented.

The comparisons between the improved bat algorithm (iBA) presented in this paper and other standard algorithms on benchmark functions show that the iBA is highly efficient, and the application to structural reliability problems such as the reliability analysis of overhead crane girder proves that results obtained with iBA are highly reliable.

A new iBA and an adaptive penalty equation for handling equality constraint are developed to determine the reliability index. In addition, the low computing time and the ease implementation of this method present great advantages from the engineering viewpoint.

This study investigates the pricing and inventory control decisions of a company that owns a coffeehouse chain providing drip coffee and a coffee manufacturing factory producing ready-to-drink (RTD) coffee. To determine a way to maximize profit, optimal pricing and inventory control strategies are studied for both the RTD coffee as well as the coffeehouse chain network.

It is assumed that the company sells only drip coffee through its coffeehouse chain and would like to launch RTD coffee via other channels such as convenience stores, supermarkets and so on to maximize its total profit. A mathematical model–based optimization is adopted to address the decision-making for the given problem situation, where the demand for both drip and RTD coffee are dependent on the values of decision variables. To solve the proposed mathematical model, particle swarm optimization (PSO) is applied due to nonlinearity of the developed model.

It is confirmed that a company can earn more profit by launching RTD coffee, even though the profit from drip coffee would reduce. In addition, the scenario analysis shows that by launching RTD coffee under various conditions, the total profits would also improve.

The value of this study is the proposed basic framework for the industry. In addition to the modeling framework and cost structure, realistic cost figures and technical details can be considered when applying the model to a practical setting.

This paper aims to represent an improved whale optimization algorithm (WOA) based on a Lévy flight trajectory and called the LWOA algorithm to solve engineering optimization problems. The LWOA makes the WOA faster, more robust and significantly enhances the WOA. In the LWOA, the Lévy flight trajectory enhances the capability of jumping out of the local optima and is helpful for smoothly balancing exploration and exploitation of the WOA. It has been successfully applied to five standard engineering optimization problems. The simulation results of the classical engineering design problems and real application exhibit the superiority of the LWOA algorithm in solving challenging problems with constrained and unknown search spaces when compared to the basic WOA algorithm or other available solutions.

In this paper, an improved WOA based on a Lévy flight trajectory and called the LWOA algorithm is represented to solve engineering optimization problems.

It has been successfully applied to five standard engineering optimization problems. The simulation results of the classical engineering design problems and real application exhibit the superiority of the LWOA algorithm in solving challenging problems with constrained and unknown search spaces when compared to the basic WOA algorithm or other available solutions.

An improved WOA based on a Lévy flight trajectory and called the LWOA algorithm is first proposed.

The purpose of this paper is to present a methodology for medical robot kinematics design developed using a knowledge‐management approach.

A classification of medical robots is proposed based on their kinematic characteristics and 76 robot specifications were collected in a catalogue. Then, having drawn a generic specifications sheet, rules were proposed to choose a structure from these specifications.

Findings are situated at several levels: the catalogue, the classification of robots with respect to their kinematic characteristics, a generic and specific specifications sheet, and an organigram to choose the most relevant structure from the specifications.

This structural synthesis represents a preliminary step in the design of medical robots which will be completed by an additional dimensional synthesis.

This work offers a new methodology for medical robots design distinct from what is usually done for medical or industrial robots design using intuition, expertise and non‐formal knowledge.

Part II contrasts the views of materiality in the Conceptual Frameworks of the IASB, FASB, IPSAS, and other framework such as the Integrated Reporting. In particular, it analyzes at what level and how differently that concept interacts with the qualitative characteristics of financial information in each of those frameworks. It looks at its pervasiveness and entity specificity, the interlock with the concept of relevance, reliability and faithful representation, completeness, understandability, neutrality, and drills down to the link to recognition.

This part then compares the definitions of materiality in different standards and contexts, to then draw a taxonomy of materiality and its attributes, such as the subject matter, thecontext of assessment, the addressees, the assessor, and the materiality test. A large part of the analysis involves the comparison between legal definitions of materiality and characterizations in the accounting, financial, and larger management contexts.

The aim of this study was to first establish foundational algebraic expressions that parametrically describe any advanced dual-energy storage–propulsion–power system (DESPPS) and then proceed to declare the array of fundamental independent variables necessary for the sizing and optimisation of such systems. Upon procurement of a pre-design-level integrated aircraft performance model and the subsequent verification against previously published high-end low-fidelity generated results, opportunity was taken in formulating a set of battery-based DESPPS related design axioms and sizing heuristics.

Derivation of algebraic expressions related to describing DESPPS architectures are based on first principles. Integrated performance modelling by way of full analytical fractional change transformations anchored according to a previously published Energy Specific Air Range (ESAR) figure-of-merit originally derived using the Breguet–Coffin differential equation for vehicular efficiency. Weights prediction of sub-systems that constitute the entire aircraft including DESPPS constituents emphasises an analytical foundation with minimal implementation of linear correlation factors or coefficients of proportionality. An iterative maximum take-off weight build-up algorithm emphasising expedient and stable convergence was fashioned. All prediction methods pertaining to integrated performance were verified according to previously published battery-based DESPPS results utilising high-end low-fidelity methods.

For all types of DESPPS, two new fundamental independent non-dimensional variables were declared: the Supplied Power Ratio (related to converted power afforded by each energy carrier); and, the Activation Ratio (describing the relative nature of utilisation with respect to time afforded by the motive power device associated with each energy source). For a given set of standalone sub-system energy conversion efficiencies, the parametric descriptor of degree-of-hybridisation (DoH) for Power was found to be solely a function of the Supplied Power Ratio, whereas in contrast, the DoH for Energy was found to be a more complex synthetic function described by comingling of Supplied Power Ratio and the Activation Ratio. Upon examination of the integrated aircraft performance model derived in this treatise, for purposes of investigating CO₂-emissions reduction potential for battery-based DESPPS using kerosene as one of the energy sources, one salient observation was maximising the ESAR figure-of-merit is not an appropriate objective or intermediary function for future optimisation work. It was found maximising block fuel reduction through the use of maximum ESAR would lead to ever diminishing design ranges and curtailment of the payload-range working capacity of the aircraft.

Opportunity is now given to design and optimise aircraft utilising any type of DESPPS architecture. It was established that designing for battery-based DESPPS aircraft can be achieved effectively in a two-stage process that may not require aircraft morphologies more exotic than the so-called “wing-and-tube”. Firstly, a suitably projected state-of-the-art aircraft with solely advanced gas-turbine technology for the propulsion and power system needs to be produced. Thereafter, a revised version of this baseline projected aircraft now using DESPPS architecture should be conceived. A recommendation related to CO₂-emissions reduction potential for battery-based DESPPS using kerosene as one of the energy sources is that during optimisation work the multi-objective formulation should comprise at least two functions: block fuel and operating economics. In all instances, it was advised that the objective function of block fuel should be tempered by an equality constraint of ESAR parity with the baseline projected aircraft using gas-turbine only technology.

A complete, unified analytical description of DESPPS that is universally applicable to any type of energy carrier has been derived and verified for battery-based dual-energy systems. Correspondingly, a set of aircraft design axioms and sizing heuristics relevant to battery-based DESPPS have been presented.

This paper aims to compare and analyse the aspects of Shariah screening methodologies within the selected Gulf Cooperation Council (GCC) countries as well as comparing the methodologies with the USA, and to examine how Shariah screening methodologies affect financing and investing activities of a firm.

Shariah screening methodologies within the selected GCC countries and between the GCC countries and the USA are compared on the basis of the data collected from secondary sources.

Design, qualification and Shariah governance set the Shariah screening methodologies within the GCC countries apart. Feasibility, duration, economic viability and funds required differentiate these Shariah screening methodologies between the GCC countries and the USA. Shariah screening methodologies implied in the USA is more stringent than in the GCC countries.

The suggestions in this study include using a longer research timeline, examining many more number of countries’ Shariah screening methodologies and exploring other types of Shariah screening methodologies.

The possibility of generalising the implementation of strict and uniform Shariah screening methodologies across all the country-specific Shariah indices amongst Muslim nations, globally, is likely to benefit all the Muslim countries, by strengthening the understanding, interaction and economic co-operation amongst these countries.

People’s needs can be tended to if Maqasid Al-Shariah (objectives of Shariah) is achieved through flexibility, dynamism and creativity within the social policy.

Aspects of Shariah screening methodologies are compared and contrasted within the selected GCC countries as well as between the GCC countries and the United States and the role of Shariah screening methodologies is examined in order to determine the extent of what is Shariah-Compliant and what is Non-Shariah Compliant for a firm.