Less detectable environmental changes in dynamic multiobjective optimisation

Multiobjective optimisation in dynamic environments is challenging due to the presence of dynamics in the problems in question. Whilst much progress has been made in benchmarks and algorithm design for dynamic multiobjective optimisation, there is a lack of work on the detectability of environmental changes and how this affects the performance of evolutionary algorithms. This is not intentionally left blank but due to the unavailability of suitable test cases to study. To bridge the gap, this work presents several scenarios where environmental changes are less likely to be detected. Our experimental studies suggest that the less detectable environments pose a big challenge to evolutionary algorithms

Evolutionary Dynamic Multi-Objective Optimisation : A survey

Peer reviewedPostprin

A Pareto-based evolutionary algorithm using decomposition and truncation for dynamic multi-objective optimization

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Maintaining a balance between convergence and diversity of the population in the objective space has been widely recognized as the main challenge when solving problems with two or more conflicting objectives. This is added by another difficulty of tracking the Pareto optimal solutions set (POS) and/or the Pareto optimal front (POF) in dynamic scenarios. Confronting these two issues, this paper proposes a Pareto-based evolutionary algorithm using decomposition and truncation to address such dynamic multi-objective optimization problems (DMOPs). The proposed algorithm includes three contributions: a novel mating selection strategy, an efficient environmental selection technique and an effective dynamic response mechanism. The mating selection considers the decomposition-based method to select two promising mating parents with good diversity and convergence. The environmental selection presents a modified truncation method to preserve good diversity. The dynamic response mechanism is evoked to produce some solutions with good diversity and convergence whenever an environmental change is detected. In the experimental studies, a range of dynamic multi-objective benchmark problems with different characteristics were carried out to evaluate the performance of the proposed method. The experimental results demonstrate that the method is very competitive in terms of convergence and diversity, as well as in response speed to the changes, when compared with six other state-of-the-art methods

A Scalable Test Suite for Continuous Dynamic Multiobjective Optimization

Dynamic multiobjective optimization (DMO) has gained increasing attention in recent years. Test problems are of great importance in order to facilitate the development of advanced algorithms that can handle dynamic environments well. However, many of the existing dynamic multiobjective test problems have not been rigorously constructed and analyzed, which may induce some unexpected bias when they are used for algorithmic analysis. In this paper, some of these biases are identified after a review of widely used test problems. These include poor scalability of objectives and, more importantly, problematic overemphasis of static properties rather than dynamics making it difficult to draw accurate conclusion about the strengths and weaknesses of the algorithms studied. A diverse set of dynamics and features are then highlighted that a good test suite should have. We further develop a scalable continuous test suite, which includes a number of dynamics or features that have been rarely considered in literature but frequently occur in real life. It is demonstrated with empirical studies that the proposed test suite are more challenging to the DMO algorithms found in the literature. The test suite can also test algorithms in ways that existing test suites cannot

Evolutionary Algorithms for Static and Dynamic Multiobjective Optimization

Many real-world optimization problems consist of a number of conflicting objectives that have to be optimized simultaneously. Due to the presence of multiple conflicting ob- jectives, there is no single solution that can optimize all the objectives. Therefore, the resulting multiobjective optimization problems (MOPs) resort to a set of trade-off op- timal solutions, called the Pareto set in the decision space and the Pareto front in the objective space. Traditional optimization methods can at best find one solution in a sin- gle run, thereby making them inefficient to solve MOPs. In contrast, evolutionary algo- rithms (EAs) are able to approximate multiple optimal solutions in a single run. This strength makes EAs good candidates for solving MOPs. Over the past several decades, there have been increasing research interests in developing EAs or improving their perfor- mance, resulting in a large number of contributions towards the applicability of EAs for MOPs. However, the performance of EAs depends largely on the properties of the MOPs in question, e.g., static/dynamic optimization environments, simple/complex Pareto front characteristics, and low/high dimensionality. Different problem properties may pose dis- tinct optimization difficulties to EAs. For example, dynamic (time-varying) MOPs are generally more challenging than static ones to EAs. Therefore, it is not trivial to further study EAs in order to make them widely applicable to MOPs with various optimization scenarios or problem properties. This thesis is devoted to exploring EAs’ ability to solve a variety of MOPs with dif- ferent problem characteristics, attempting to widen EAs’ applicability and enhance their general performance. To start with, decomposition-based EAs are enhanced by incorpo- rating two-phase search and niche-guided solution selection strategies so as to make them suitable for solving MOPs with complex Pareto fronts. Second, new scalarizing functions are proposed and their impacts on evolutionary multiobjective optimization are exten- sively studied. On the basis of the new scalarizing functions, an efficient decomposition- based EA is introduced to deal with a class of hard MOPs. Third, a diversity-first- and-convergence-second sorting method is suggested to handle possible drawbacks of convergence-first based sorting methods. The new sorting method is then combined with strength based fitness assignment, with the aid of reference directions, to optimize MOPs with an increase of objective dimensionality. After that, we study the field of dynamic multiobjective optimization where objective functions and constraints can change over time. A new set of test problems consisting of a wide range of dynamic characteristics is introduced at an attempt to standardize test environments in dynamic multiobjective optimization, thereby aiding fair algorithm comparison and deep performance analysis. Finally, a dynamic EA is developed to tackle dynamic MOPs by exploiting the advan- tages of both generational and steady-state algorithms. All the proposed approaches have been extensively examined against existing state-of-the-art methods, showing fairly good performance in a variety of test scenarios. The research work presented in the thesis is the output of initiative and novel attempts to tackle some challenging issues in evolutionary multiobjective optimization. This re- search has not only extended the applicability of some of the existing approaches, such as decomposition-based or Pareto-based algorithms, for complex or hard MOPs, but also contributed to moving forward research in the field of dynamic multiobjective optimiza- tion with novel ideas including new test suites and novel algorithm design

EVOLUTIONARY MULTI-OBJECTIVE OPTIMIZATION IN STATIC AND DYNAMIC ENVIRONMENTS

Ph.DDOCTOR OF PHILOSOPH

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Passive intelligent kinetic external Dynamic shade design for improving indoor comfort and minimizing energy consumption

In humid subtropical climates with a green environment, windows are the most dominant envelope elements affecting indoor visual and thermal comfort and visual connection to the outdoors. This research aims to optimize a dynamic external shading system for north-facing windows in Sydney, Australia, which acts automatically in eight predefined scenarios in response to indoor comfort conditions. The method of investigation was simulating a multi-objective optimization approach using Non-dominated Sorting Particle Swarm Optimization (NSPSO) to assess visual and thermal comfort along with energy usage and view of the outside. A combination of human and sensor assessments were applied to validate the simulations. A set of sensors and High Quality (HQ) cameras fed the system input to operate the shade. Simulations and field measurements demonstrated that optimized shading scenarios brought average yearly reductions of 71.43%, 72.52%, and 1.78% in Annual Solar Exposure, Spatial Daylight Glare, and LEED Quality View, respectively, without sacrificing Daylight Autonomy. Moreover, yearly improvements of 71.77% in cooling demand were achieved. The downside of the shading system was an increase of 0.80% in heating load and 23.76% in lighting electricity, which could be a trade-off for improved comfort and energy savings. This study investigated the effect of dynamic external shade on visual and thermal comfort together with energy usage and view, which has not been investigated for southern-hemisphere dwellings. A camera-sensor-fed mechanism operated the external shade automatically, providing indoor comfort without manual operation

Real-time Operational Response Methodology for Reducing Failure Impacts in Water Distribution Systems

Interruption to water services and low water pressure conditions are commonly observed problems in water distribution systems (WDSs). Of particular concern are the unplanned events, such as pipe bursts. The current regulation in the UK requires water utilities to provide reliable water service to consumers resulting in as little as possible interruptions and of as short possible duration. All this pushes water utilities toward developing and using smarter responses to these events, based on advanced tools and solutions. All with the aim to change network management style from reactive to a proactive, and reduce water losses, optimize energy use and provide better services for consumers. This thesis presents a novel methodology for efficient and effective operational, short time response to an unplanned failure event (such as pipe burst) in a WDS. The proposed automated, near real-time operational response methodology consists of isolating the failure event followed by the recovery of the affected system area by restoring the flows and pressures to normal conditions. The isolation is typically achieved by manipulating the relevant on/off valves that are located closely to the event location. The recovery involves selecting an optimal combination of suitable operational network interventions. These are selected from a number of possible options with the aim to reduce the negative impact of the failure over a pre-specified time horizon. The intervention options considered here include isolation valve manipulations, changing the pressure reducing valve’s (PRV) outlet pressure and installation and use of temporary overland bypasses from a nearby hydrant(s) in an adjacent, unaffected part of the network. The optimal mix of interventions is identified by using a multi-objective optimization approach driven by the minimization of the negative impact on the consumers and the minimization of the corresponding number of operational interventions (which acts as a surrogate for operational costs). The negative impact of a failure event was quantified here as a volume of water undelivered to consumers and was estimated by using a newly developed pressure-driven model (PDM) based hydraulic solver. The PDM based hydraulic solver was validated on a number of benchmark and real-life networks under different flow conditions. The results obtained clearly demonstrate its advantages when compared to a number of existing methods. The key advantages include the simplicity of its implementation and the ability to predict network pressures and flows in a consistently accurate, numerically stable and computationally efficient manner under both pressure-deficient and normal-flow conditions and in both steady-state and extended period simulations. The new real-time operational response methodology was applied to a real world water distribution network of D-Town. The results obtained demonstrate the effectiveness of the proposed methodology in identifying the Pareto optimal network type intervention strategies that could be ultimately presented to the control room operator for making a suitable decision in near real-time.Kurdistan Regional Government in Iraq, Ministry of High Education and Scientific Research, Human Capacity Development Program (HCDP)