Andrew Lewis

• PhD
• Professor (Associate) at Griffith University

[This corrects the article DOI: 10.1371/journal.pone.0262402.].

Optimising the use of natural resources for food production in the context of changing climate is an increasingly important issue. Optimisation techniques have been shown to be remarkably effective for planning problems, and tools regional planners and farmers can use to determine the viability of agricultural land use planning into the future. Thi...

Food security has become a concerning issue because of global climate change and increasing populations. Agricultural production is considered one of the key factors that affects food security. The changing climate has negatively affected agricultural production, which accelerates food shortages. The supply of agricultural commodities can be heavil...

Climate change is impacting people’s lives, with management of water resources and food security being major concerns for the future of many countries. In this paper, future water availability, crop water needs, yields, market costs and returns of current crops in a case study area in Australia are evaluated under future climatic conditions. The pr...

Climate change is impacting people's lives, with management of water resources and food security being major concerns for the future of many countries. In this paper, future water availability, crop water needs, yields, market costs and returns of current crops in a case study area in Australia is evaluated under future climatic conditions. The pre...

Climate change is impacting people's lives, with food security being a major concern for the future of many countries. In this paper, production capacity of current crops in a case study area in Australia is evaluated under future climatic condition. The predictive methods, on which this work is based, have the advantage of being robust---they able...

In many parts of the world, conditions for small scale agriculture are worsening, creating challenges in achieving consistent yields. The use of automated decision support tools, such as Bayesian Belief Networks (BBNs), can assist producers to respond to these factors. This paper describes a decision support system developed to assist farmers on th...

Considering a temporal dimension allows for the delivery of rolling solutions to complex real-world problems. Moving forward in time brings uncertainty, and large margins for potential error in solutions. For the multi-year crop planning problem, the largest uncertainty is how the climate will change over coming decades. The innovation this paper p...

In the age of climate change, increasing populations and more limited resources, efficient agricultural production is being sought by farmers across the world. In the case of smallholder farms with limited capacity to cope with years of low production, this is even more important. To help to achieve this aim, data analytics and decision support sys...

Optimisation problems usually take the form of having a single or multiple objectives with a set of constraints. The model itself concerns a single problem for which the best possible solution is sought. Problems are usually static in the sense that they do not consider changes over time in a cumulative manner. Dynamic optimisation problems to inco...

This chapter starts with the inspiration and main mechanisms of one of the most well-regarded combinatorial optimization algorithms called Ant Colony Optimizer (ACO). This algorithm is then employed to find the optimal path for an AUV. In fact, the problem investigated is a real-world application of the Traveling Salesman Problem (TSP).

The Particle Swarm Optimization (PSO) is one of the most well-regarded algorithms in the literature of meta-heuristics. This algorithm mimics the navigation and foraging behaviour of birds in nature. Despite the simple mathematical model, it has been widely used in diverse fields of studies to solve optimization problems. There is a tremendous numb...

This book covers the conventional and most recent theories and applications in the area of evolutionary algorithms, swarm intelligence, and meta-heuristics. Each chapter offers a comprehensive description of a specific algorithm, from the mathematical model to its practical application. Different kind of optimization problems are solved in this boo...

The automated design of meander line RFID antennas is a discrete self-avoiding walk (SAW) problem for which efficiency is to be maximized while resonant frequency is to be minimized. This work presents a novel exploration of how discrete local search may be incorporated into a continuous solver such as differential evolution (DE). A prior DE algori...

The effects of climate change have been much speculated on in the past few years. Consequently, there has been intense interest in one of its key issues of food security into the future. This is particularly so given population increase, urban encroachment on arable land, and the degradation of the land itself. Recently, work has been done on predi...

Emergency vehicle (EV) services save lives around the world. The necessary fast response of EVs requires minimising travel time. Preempting traffic signals can enable EVs to reach the desired location quickly. Most of the current research tries to decrease EV delays but neglects the resulting negative impacts of the preemption on other vehicles in...

The low-pressure sprinklers have been widely used to replace the high-pressure impact sprinklers in the lateral move sprinkler irrigation system due to its low operating cost and high efficiency. However, runoff losses under the low-pressure sprinkler irrigation machine can be significant. This study aims to evaluate the performance of the variable...

Due to restrictions and limitations on agricultural water worldwide, one of the most effective ways to conserve water in this sector is to reduce the water losses and improve irrigation uniformity. Nowadays, the low-pressure sprinkler has been widely used to replace the high-pressure impact sprinklers in lateral move sprinkler irrigation systems du...

Test problems are considered essential when designing optimisation algorithms. The two main conflicting characteristics of a proper test function are simplicity and complexity. The former feature is to allow analysing the behaviour of algorithms, whereas the latter is to mimic real-world problems. Despite the importance of the test functions, howev...

Multi-objective problems with conflicting objectives cannot be effectively solved by aggregation-based methods. The answer to such problems is a Pareto optimal solution set. Due to the difficulty of solving multi-objective problems using multi-objective algorithms and the lack of enough expertise, researchers in different fields tend to aggregative...

Efficiencies in farming practice in many parts of South East Asia can make substantial, positive differences to villages and communities. The use of automated decision-assistance tools such as Bayesian Belief Networks (BBNs) can help to accomplish this. For the problem described herein, farmers attempt to grow both rice and shrimp crops in the same...

Robust optimisation refers to the process of finding optimal solutions that have the lowest sensitivity to possible perturbations. In a multi-objective search space the robust optimal solutions should have the least dispersion on all of the objectives, making it a more challenging problem than in a single-objective search space. This paper establis...

The International Joint Conference on Neural Networks (IJCNN) was held in Anchorage (Alaska) in May 2017. This top conference in the field of neural networks included many tracks and special sessions. In particular, a special session on Machine Learning Methods Neural Networks applied to Vision and Robotics (MLMVR) was organized by the authors rece...

Hand posture estimation is a building block in hand gesture detection systems. One of the most popular techniques in this field is to generate a 3D hand model and find its optimal structure using an optimisation algorithm. The main advantages of such methods are flexibility to model complex hand postures, ability to better address occlusions, and e...

Water as a resource is becoming increasingly more valuable given the changes in global climate. In an agricultural sense, the role of water is vital to ensuring food security. Therefore the management of it has become a subject of increasing attention and the development of effective tools to support participative decision-making in water managemen...

Hand posture estimation is an important step in hand gesture detection. It refers to the process of modeling hand in computer to accurately represent the actual hand obtained from an acquisition device. In the literature, several objective functions (mostly based on silhouette or point cloud) have been used to formulate and solve the problem of han...

This paper proposes an optimisation algorithm called Grasshopper Optimisation Algorithm (GOA) and applies it to challenging problems in structural optimisation. The proposed algorithm mathematically models and mimics the behaviour of grasshopper swarms in nature for solving optimisation problems. The GOA algorithm is first benchmarked on a set of t...

Increasing human populations and the continual change of the Earth's climate has meant that food security is becoming an increasingly important issue. One of the main factors contributing to food security is the availability of water for agricultural purposes. Recently, a few models have been proposed for water management problems in agricultural c...

The power to solve intractable optimisation problems is often found through population based evolutionary methods. These include, but are not limited to, genetic algorithms, particle swarm optimisation, differential evolution and ant colony optimisation. While showing much promise as an effective optimiser, extremal optimisation uses only a single...

This paper proposes a novel nature-inspired meta-heuristic optimization algorithm, called Whale Optimization Algorithm (WOA), which mimics the social behavior of humpback whales. The algorithm is inspired by the bubble-net hunting strategy. WOA is tested with 29 mathematical optimization problems and 6 structural design problems. Optimization resul...

The International Joint Conference on Neural Networks (IJCNN) is the premier professional conference in the field of neural networks. A special session on Neural Networks for Vision and Robotics (NNVR) was organized with a large volume of high quality papers. A small number of outstanding papers presented at this special session were invited to sub...

The run time for many optimisation algorithms, particularly those that explicitly consider mul- tiple objectives, can be impractically large when applied to real world problems. This paper reports an investigation into the behaviour of Multi-Objective Particle Swarm Optimisation (MOPSO), which seeks to reduce the number of objective function evalua...

Real world problems have usually multiple objectives. These objective functions are of- ten in conflict, making them highly challenging in terms of determining optimal solutions and analysing solutions obtained. In this work Multi-objective Particle Swarm Optimisation (MOPSO) is employed to optimise the shape of marine propellers for the first time...

Different evolutionary algorithms, by their very nature, will have different search trajectory characteristics. Understanding these particularly for real world problems gives researchers and practitioners valuable insights into potential problem domains for the various algorithms, as well as an understanding for potential hybridisation. In this stu...

This paper presents a generalised parametrisation as well as an approach to computational optimisation for small, planar antennas. A history of previous, more limited antenna optimisation techniques is discussed and a new parametrisation introduced in this context. Validation of this new approach against previously developed structures is provided...

Abstract Despite the significant number of benchmark problems for evolutionary multi-objective optimisation algorithms, there are few in the field of robust multi-objective optimisation. This paper investigates the characteristics of the existing robust multi-objective test problems and identifies the current gaps in the literature. It is observed...

This paper first identifies a substantial gap in the literature of robust optimisation relating to the simplicity, low-dimensionality, lack of bias, lack of deceptiveness, and lack of multi-modality of test problems. Five obstacles and difficulties such as desired number of variables, bias, deceptiveness, multi-modality, and flatness are then propo...

Current robust optimisation techniques can be divided into two main groups: algorithms that rely on previously sampled points versus those that need additional function evaluations to confirm robustness of solutions during optimisation. This paper first identifies and investigates the drawbacks of these two methods: unreliability for the first and...

Use of an optimization algorithm to improve performance of antennas and electromagnetic structures usually ends up in planar unusual shapes. Using rectangular conducting elements the proposed structures sometimes have connections with only one single point in common between two neighboring areas. The single point connections (point crossing) can af...

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 differen...

In meta-heuristic optimisation, the robustness of a particular solution can be confirmed by re-sampling, which is reliable but computationally expensive, or by reusing neighbourhood solutions, which is cheap but unreliable. This work proposes a novel metric called the confidence measure to increase the reliability of the latter method, defines new...

This paper presents an overview of recent advances in engineering design practice, in particular the emergence of the widespread use of computational optimisation methods. It outlines " human-in-the-loop " optimisation, and describes the visualisation methods that underpin effective interaction. Key features are presented of a web-based interactive...

Island conservation management is a truly multidisciplinary problem that requires considerable knowledge of the characteristics of the ecosystem, species and their interactions. Nevertheless, this can be translated into an optimisation problem. Essentially, within a limited budget, a manager needs to select the conservation actions according to exp...

RFID antennas are ubiquitous, so exploring the space of high efficiency and low resonant frequency antennas is an important multiobjective problem. Previous work has shown that the continuous solver differential evolution (DE) can be successfully applied to this discrete problem, but has difficulty exploring the region of solutions with lowest reso...

One heuristic evolutionary algorithm recently proposed is the gravitational search algorithm (GSA), inspired by the gravitational forces between masses in nature. This algorithm has demonstrated superior performance among other well-known heuristic algorithms such as particle swarm optimisation and genetic algorithm. However, slow exploitation is a...

Performance metrics are essential for quantifying the performance of optimization algorithms in the field of evolutionary multi-objective optimization. Such metrics allow researchers to compare different algorithms quantitatively. In the field of robust multi-objective optimization, however, there is currently no performance metric despite its sign...

The biogeography-based optimisation (BBO) algorithm is a novel evolutionary algorithm inspired by biogeography. Similarly, to other evolutionary algorithms, entrapment in local optima and slow convergence speed are two probable problems it encounters in solving challenging real problems. Due to the novelty of this algorithm, however, there is littl...

In existing work, Artificial Neural Networks (ANNs) are often used to model objective functions for Multi-Objective Particle Swarm Optimisation (MOPSO) or MOPSO is used to aid in ANN-training. We instead use an ANN to guide the optimisation algorithm by deciding if a trial solution is worthy of full evaluation. This should be particularly helpful f...

Local search is an integral part of many meta-heuristic strategies that solve single objective optimisation problems. Essentially, the meta-heuristic is responsible for generating a good starting point from which a greedy local search will find the local optimum. Indeed, the best known solutions to many hard problems (such as the travelling salesma...

Interactive Multi-Objective Optimisation is an increasing field of evolutionary and swarm intelligence-based algorithms. By involving a human decision, a set of relevant non-dominated points can often be acquired at significantly lower computational costs than with a posteriori algorithms. A rarely addressed issue in interactive optimisation is the...

In this paper, a modified particle swarm optimization (PSO) algorithm called autonomous groups particles swarm optimization (AGPSO) is proposed to further alleviate the two problems of trapping in local minima and slow convergence rate in solving high-dimensional problems. The main idea of AGPSO algorithm is inspired by individuals’ diversity in bi...

In real-world computational engineering design, it is often necessary to consider a large number of design parameters to describe and model the product under investigation, and a large number of objective functions to express the strongly competing performance metrics. As a result, when we successfully perform multi-objective optimization studies,...

The Multi-Layer Perceptron (MLP), as one of the most-widely used Neural Networks (NNs), has been applied to many practical problems. The MLP requires training on specific applications, often experiencing problems of entrapment in local minima, convergence speed, and sensitivity to initialization. This paper proposes the use of the recently develope...

Transfer functions are considered the simplest and cheapest operators in designing discrete heuristic algorithms. The main advantage of such operators is the maintenance of the structure and other continuous operators of a continuous algorithm. However, a transfer function may show different behaviour in various heuristic algorithms. This paper inv...

This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three...

Fundamental antenna limits of the gain-bandwidth product are derived from polarizability calculations. This electrostatic technique has significant value in many antenna evaluations. Polarizability is not available in closed form for most antenna shapes and no commercial electromagnetic packages have this facility. Numerical computation of the pola...

This letter proposes a novel framework for designing photonic crystal waveguides (PCWs), in which three components optimize and analyze the structure of PCWs. The proposed components are a multi-objective formulator, a multi-objective optimizer, and a multi-objective analyzer. A ring-shaped hole PCW is chosen as the case study, but the proposed fra...

El cómputo de alto rendimiento es una necesidad para el desarrollo de investigaciones con grandes volúmenes de datos. La creciente demanda de este tipo de resultados ha impulsado a varios centros de investigación a poner en funcionamiento recursos de cómputo de alto rendimiento. En Cuba no existe una solución definitiva que permita a todos los cent...

The fundamental limit for small antennas provides a guide to the effectiveness of designs. Gustafsson et al, Yaghjian et al, and Mohammadpour-Aghdam et al independently deduced a variation of the Chu-Harrington limit for planar antennas in different forms. Using a multi-parameter optimisation technique based on the ant colony algorithm, planar, mea...

This paper presents the integration of human-machine interaction technologies within a virtual reality environment to allow for real-time manipulation of 3D objects using different gestures. We demonstrate our approach by developing a fully operational, natural user interface (NUI) system, which provides a front-end framework for back-end applicati...

Fundamental antenna limits of the gain-bandwidth product are derived from polarizability calculations. This electrostatic technique has significant value in many antenna evaluations. Polarizability is not available in closed form for most antenna shapes and no commercial electromagnetic packages have this facility. Numerical computation of the pola...

We propose the design of a real-time system to recognize and interpret hand gestures. The acquisition devices are low cost 3D sensors. 3D hand pose segmentation, characterization and tracking will be implemented using the growing neural gas (GNG) structure. The capacity of the system to obtain information with a high degree of freedom allows the en...

New lower physical bounds on the quality factor Q were recently reported in the literature by Yaghjian et~al, and Mohammadpour et~al. Due to the approximations made in derivation of the limits, examination of the limits for different antennas is very important but has not been reported. In this paper, physical bounds are examined for a set of meand...

Variants of the multi-objective particle swarm optimisation (MOPSO) algorithm are investigated, mainly focusing on swarm topology, to optimise the well-known 2D airfoil design problem. The topologies used are global best, local best, wheel, and von Neumann. The results are compared to the non-dominated sorting genetic algorithm (NSGA-ii) and multi-...

The most common approach to decision making in muIti-objective optimisation with metaheuristics is a posteriori preference articulation. Increased model complexity and a gradual increase of optimisation problems with three or more objectives have revived an interest in progressively interactive decision making, where a human decision maker interact...

Particle Swarm Optimization (PSO) is one of the most widely used heuristic algorithms. The simplicity and inexpensive computational cost makes this algorithm very popular and powerful in solving a wide range of problems. The binary version of this algorithm has been introduced for solving binary problems. The main part of the binary version is a tr...

This paper proposes a novel tri-objective approach for optimizing the structure of line defect Photonic Crystal Waveguides (PCW). A nature-inspired algorithm called Multi-Objective Particle Swarm Optimization (MOPSO) is employed as the optimizer. The three objectives considered are maximization of group index, maximization of bandwidth, and minimiz...

The issue of studying the effect of fixing the length of the selected feature subsets using ant colony optimization (ACO) has not yet been studied. This paper addresses this concern by demonstrating four points that are: 1) determining the optimal feature subset, 2) determining the length of the subsets in ACO for subset selection problems, 3) diff...

This article proposes a method for designing electromagnetic compatibility shielding enclosures using a peer-to-peer based distributed optimization system based on a modified particle swarm optimization algorithm. This optimization system is used to obtain optimal solutions to a shielding enclosure design problem efficiently with respect to both el...

This paper describes a novel problem formulation and specialised Multi- Objective Particle Swarm Optimisation (MOPSO) algorithm to discover the reaction pathway and Transition State (TS) of small molecules. Transition states play an important role in computational chemistry and their discovery represents one of the big challenges in computational c...

Extremal Optimisation (EO) is a recent nature-inspired meta-heuristic whose search method is especially suitable to solve combinatorial optimisation problems. This paper presents the implementation of a multi-objective version of EO to solve the real-world Radio Frequency IDentification (RFID) antenna design problem, which must maximise efficiency...

Differential evolution (DE) has been traditionally applied to solving benchmark continuous optimisation functions. To enable it to solve a combinatorially oriented design problem, such as the construction of effective radio frequency identification antennas, requires the development of a suitable encoding of the discrete decision variables in a con...

In this paper we introduce a novel design for a translational medical research ecosystem. Translational medical research is an emerging field of work, which aims to bridge the gap between basic medical science research and clinical research/patient care. We analyze the key challenges of digital ecosystems for translational research, based on real w...

This paper presents the impact of twins and the measures for their removal from the population of genetic algorithm (GA) when applied to effective conformational searching. It is conclusively shown that a twin removal strategy for a GA provides considerably enhanced performance when investigating solutions to complex ab initio protein structure pre...

Ant colony optimisation has traditionally been used to solve problems that have few/light constraints or no constraints at all. Algorithms to maintain and restore feasibility have been successfully applied to such problems. Set partitioning is a very constrained combinatorial optimisation problem, for which even feasible solutions are difficult to...

Ant colony optimisation is often used to construct solutions to problems for which it has no a-priori knowledge. However, decision makers and designers often have insight and practical experience concerning what constitutes good and acceptable solutions. In this paper we exploit this knowledge by applying a pre-seeding procedure to the initial pher...

It is only relatively recently that extremal optimisation (EO) has been applied to combinatorial optimisation problems. As such, there have been only a few attempts to extend the paradigm to include standard search mechanisms that are routinely used by other techniques such as genetic algorithms, tabu search and ant colony optimisation. The key way...