Khairul Alam

An adaptive constraint handling approach embedded MOEA/D

This paper proposes an efficient, adaptive constraint handling approach that can be used within the class of evolutionary multi-objective optimization (EMO) algorithms. The proposed constraint handling approach is presented within the framework of one of the most successful algorithms i.e. multi-objective evolutionary algorithm based on decomposition (MOEA/D) [1]. The constraint handling mechanism adaptively decides on the violation threshold for comparison. The violation threshold is based on the type of constraints, size of the feasible space and the search outcome. Such a process intrinsically treats constraint violation and objective function values separately and adds a selection pressure, wherein infeasible solutions with violations less than the identified threshold are considered at par with feasible solutions. As illustrated, the constraint handling scheme extends the current capability of MOEA/D to deal with constraints. The performance of the algorithm is illustrated using 10 commonly studied benchmark problems and a real-world constraint optimization problem, and compared with the results obtained using yet another commonly used form i.e. Nondominated Sorting Genetic Algorithm (NSGA-II).

Design and construction of an autonomous underwater vehicle

Autonomous underwater vehicles (AUVs) are becoming increasingly popular for ocean exploration, military and industrial applications. In particular, AUVs are becoming an attractive option for underwater search and survey operations as they are inexpensive compared to manned vehicles. Previous attempts on AUV designs have focused primarily on functional designs while very little research has been directed to identify optimum designs. This paper presents an optimization framework for the design of AUVs using two state-of-the-art population based optimization algorithms, namely non-dominated sorting genetic algorithm (NSGA-II) and infeasibility driven evolutionary algorithm (IDEA). The framework is subsequently used to identify the optimal design of a torpedo-shaped AUV with an overall length of 1.3m. The preliminary design identified through the process of optimization is further analyzed with the help of a computer-aided design tool, CATIA to generate a detailed design. The detailed design has since then been built and is currently undergoing trials. The flexibility of the proposed framework and its ability to identify optimum preliminary designs of AUVs with different sets of user requirements are also demonstrated.

A new robust design optimization approach for unmanned underwater vehicle design

Design of unmanned underwater vehicles is a complex and challenging task. While optimization methods can be applied to identify designs which are faster, more manoeuvrable and flexible to deal with various mission profiles, a practical realization requires the design to be robust, i.e. one with the best average performance under expected parametric variations. In this paper, an optimization framework for the design of underwater vehicles is presented. The framework is subsequently used to identify optimal and robust optimal designs of a small-scale (length nominally less than 500 mm) and light-weight (less than 0.5 kg) toy submarine. The submarine design described in the paper relies heavily on the use of off-the-shelf components in an attempt to contain cost. The differences between the optimal design and the robust optimal design are discussed in detail. The designs identified through the process of optimization are compared with an existing toy submarine to highlight the benefits offered by the present approach. The framework has been used to design two underwater vehicles which are currently being built by the research group.

Design of a toy submarine using underwater vehicle design optimization framework

This paper presents a framework for optimum design of a small, low-cost, light-weight toy submarine for recreational purposes. Two state of the art optimization algorithms namely Non-dominated sorting genetic algorithm (NSGA-II) and Infeasibility driven evolutionary algorithm (IDEA) have been used in this study to carry out optimization of the toy submarine design. Single objective formulation of the toy submarine design problem is considered in this paper to identify designs with minimum drag and internal clash free assembly. The flexibility of the proposed framework and its ability to identify optimum preliminary designs of a toy submarine are demonstrated. Design identified through the process of optimization is compared with an existing toy submarine to highlight the benefits offered by the present approach.

Use of Infeasible Solutions During Constrained Evolutionary Search: A Short Survey

Most real world optimization problems involve constraints and constraint handling has long been an area of active research. While older techniques explicitly preferred feasible solutions over infeasible ones, recent studies have uncovered some shortcomings of such strategies. There has been a growing interest in the efficient use of infeasible solutions during the course of search and this paper presents of short review of such techniques. These techniques prefer good infeasible solutions over feasible solutions during the course of search or a part of it. The review looks at major reported works over the years and outlines how these preferences have been dealt in various stages of the solution process, viz, problem formulation, parent selection/recombination and ranking/selection. A tabular summary is then presented for easy reference to the work in this area.

An Approach to Identify Six Sigma Robust Solutions of Multi/Many-Objective Engineering Design Optimization Problems

In order to be practical, solutions of engineering design optimization problems must be robust, i.e., competent and reliable in the face of uncertainties. While such uncertainties can emerge from a number of sources (imprecise variable values, errors in performance estimates, varying environmental conditions, etc.), this study focuses on problems where uncertainties emanate from the design variables. While approaches to identify robust optimal solutions of single and multi-objective optimization problems have been proposed in the past, we introduce a practical approach that is capable of solving robust optimization problems involving many objectives building on authors’ previous work. Two formulations of robustness have been considered in this paper, (a) feasibility robustness (FR), i.e., robustness against design failure and (b) feasibility and performance robustness (FPR), i.e., robustness against design failure and variation in performance. In order to solve such formulations, a decomposition based evolutionary algorithm (DBEA) relying on a generational model is proposed in this study. The algorithm is capable of identifying a set of uniformly distributed nondominated solutions with different sigma levels (feasibility and performance) simultaneously in a single run. Computational benefits offered by using polynomial chaos (PC) in conjunction with Latin hypercube sampling (LHS) for estimating expected mean and variance of the objective/constraint functions has also been studied in this paper. Last, the idea of redesign for robustness has been explored, wherein selective component(s) of an existing design are altered to improve its robustness. The performance of the strategies have been illustrated using two practical design optimization problems, namely, vehicle crash-worthiness optimization problem (VCOP) and a general aviation aircraft (GAA) product family design problem.

Practical application of an evolutionary algorithm for the design and construction of a six-inch submarine

Unmanned underwater vehicles (UUVs) are becoming an attractive option for maritime search and survey operations as they are cheap and efficient compared to conventional use of divers or manned submersibles. Consequently, there has been a growing interest in UUV research among scientific and engineering communities. Although UUVs have received significant research interest in recent years, limited attention has been paid towards design and development of mini/micro UUVs (usually less than 1 foot in length). Micro unmanned underwater vehicles (μUUVs) are particularly attractive for deployment in extraordinarily confined spaces such as inspection of intricate underwater structures, ship wrecks, oil pipe lines or extreme hazardous areas. This paper considers previous work done in the field of miniature UUVs and presents an optimization framework for preliminary design of that class of UUVs. A state-of-the-art optimization algorithm namely infeasibility driven evolutionary algorithm (IDEA) is used to carry out optimization of the μUUV designs. The framework is subsequently used to identify optimal design of a torpedo-shaped μUUV with an overall length of six inches (152.4 mm). The preliminary design identified through the process of optimization is further analyzed with the help of a computer-aided design tool to come up with a detailed design. The final design has since then been built and is currently undergoing trials.

An evolutionary approach for the design of autonomous underwater vehicles

Autonomous underwater vehicles (AUVs) are becoming an attractive option for increasingly complex and challenging underwater search and survey operations. To meet the emerging demands of AUV mission requirements, design and tradeoff complexities, there is an increasing interest in the use of multidisciplinary design optimization (MDO) strategies. While optimization techniques have been applied successfully to a wide range of applications spanning various fields of science and engineering, there is very limited literature on optimization of AUV designs. Presented in this paper is an evolutionary approach for the design optimization of AUVs using two stochastic, population based optimization algorithms. The proposed approach is capable of modelling and solving single and multi-objective constrained formulations of the AUV design problems based on different user and mission requirements. Two formulations of the AUV design problem are considered to identify designs with minimum drag and internal clash-free assembly. The flexibility of the proposed scheme and its ability to identify optimum preliminary designs are highlighted in this paper.

Design Optimization of an Unmanned Underwater Vehicle Using Low- and High-Fidelity Models

Design optimization of an unmanned underwater vehicle (UUV) is a complex and a computationally expensive exercise that requires the identification of optimal vehicle dimensions offering the best tradeoffs between the objectives, while satisfying the set of design constraints. Although hull form optimization of marine vessels has long been an active area of research, limited attempts in the past have focused on the design optimization of UUVs and there are even fewer reports on the use of high-fidelity analysis methods within the course of optimization. While it is understood that the high-fidelity analysis is more accurate, they also tend to be far more computationally expensive. Thus, it is important to identify when a high-fidelity analysis is required as opposed to a low-fidelity estimate. The work reported in this paper is an extension of the authors previous work of a design optimization framework, where the design problem was solved using a low-fidelity model based on empirical estimates of drag. In this paper, the framework is extended to deal with high-fidelity estimates derived through seamless integration of computer-aided design, meshing and computational fluid dynamics analysis tools i.e., computer aided 3-D interactive application, ICEM, and FLUENT. The effects of using low-fidelity and high-fidelity analyses are studied in depth using a small-scale (length nominally less than 400 mm) and light-weight (less than 450 g) toy submarine. Useful insights on possible means to identify appropriateness of fidelity models via correlation measures are proposed. The term optimality used in this paper refers to optimal hull form shapes that satisfy placement of a set of prescribed internal components.

Re-design for Robustness: An Approach Based on Many Objective Optimization

Re-Design for Robustness (RDR) represents a practical class of problems, where a limited set of components of an existing product are re-designed to improve the overall robustness of the product. RDR is still a common inefficient, expensive and a time consuming industry ritual, where component sensitivities are sequentially analyzed and altered with human experts in loop. In this paper, we introduce an automated approach, wherein a trade-off set of design variants (varying number of altered components) spanning the entire a range of feasibility and performance robustness are identified using a decomposition based evolutionary optimization algorithm. The benefits offered by the approach are highlighted using two re-design optimization problems from the automotive industry.