Parallel Multi-Objective Evolutionary Algorithms: A Comprehensive Survey

Multi-Objective Evolutionary Algorithms (MOEAs) are powerful search techniques that have been extensively used to solve difficult problems in a wide variety of disciplines. However, they can be very demanding in terms of computational resources. Parallel implementations of MOEAs (pMOEAs) provide considerable gains regarding performance and scalability and, therefore, their relevance in tackling computationally expensive applications. This paper presents a survey of pMOEAs, describing a refined taxonomy, an up-to-date review of methods and the key contributions to the field. Furthermore, some of the open questions that require further research are also briefly discussed

Overview of Multi-Objective Optimization Approaches in Construction Project Management

The difficulties that are met in construction projects include budget issues, contractual time constraints, complying with sustainability rating systems, meeting local building codes, and achieving the desired quality level, to name but a few. Construction researchers have proposed and construction practitioners have used optimization strategies to meet various objectives over the years. They started out by optimizing one objective at a time (e.g., minimizing construction cost) while disregarding others. Because the objectives of construction projects often conflict with each other, single-objective optimization does not offer practical solutions as optimizing one objective would often adversely affect the other objectives that are not being optimized. They then experimented with multi-objective optimization. The many multi-objective optimization approaches that they used have their own advantages and drawbacks when used in some scenarios with different sets of objectives. In this chapter, a review is presented of 16 multi-objective optimization approaches used in 55 research studies performed in the construction industry and that were published in the period 2012–2016. The discussion highlights the strengths and weaknesses of these approaches when used in different scenarios

A self-organizing weighted optimization based framework for large-scale multi-objective optimization

The solving of large-scale multi-objective optimization problem (LSMOP) has become a hot research topic in evolutionary computation. To better solve this problem, this paper proposes a self-organizing weighted optimization based framework, denoted S-WOF, for addressing LSMOPs. Compared to the original framework, there are two main improvements in our work. Firstly, S-WOF simplifies the evolutionary stage into one stage, in which the evaluating numbers of weighted based optimization and normal optimization approaches are adaptively adjusted based on the current evolutionary state. Specifically, regarding the evaluating number for weighted based optimization (i.e., t1), it is larger when the population is in the exploitation state, which aims to accelerate the convergence speed, while t1 is diminishing when the population is switching to the exploration state, in which more attentions are put on the diversity maintenance. On the other hand, regarding the evaluating number for original optimization (i.e., t2), which shows an opposite trend to t1, it is small during the exploitation stage but gradually increases later. In this way, a dynamic trade-off between convergence and diversity is achieved in S-WOF. Secondly, to further improve the search ability in the large-scale decision space, an efficient competitive swarm optimizer (CSO) is implemented in S-WOF, which shows efficiency for solving LSMOPs. Finally, the experimental results have validated the superiority of S-WOF over several state-of-the-art large-scale evolutionary algorithms

An overview of population-based algorithms for multi-objective optimisation

In this work we present an overview of the most prominent population-based algorithms and the methodologies used to extend them to multiple objective problems. Although not exact in the mathematical sense, it has long been recognised that population-based multi-objective optimisation techniques for real-world applications are immensely valuable and versatile. These techniques are usually employed when exact optimisation methods are not easily applicable or simply when, due to sheer complexity, such techniques could potentially be very costly. Another advantage is that since a population of decision vectors is considered in each generation these algorithms are implicitly parallelisable and can generate an approximation of the entire Pareto front at each iteration. A critique of their capabilities is also provided

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

Optimized task scheduling based on hybrid symbiotic organisms search algorithms for cloud computing environment

In Cloud Computing model, users are charged according to the usage of resources and desired Quality of Service (QoS). Task scheduling algorithms are responsible for specifying adequate set of resources to execute user applications in the form of tasks, and schedule decisions of task scheduling algorithms are based on QoS requirements defined by the user. Task scheduling problem is an NP-Complete problem, due to the NP-Complete nature of task scheduling problems and huge search space presented by large scale problem instances, many of the existing solution algorithms incur high computational complexity and cannot effectively obtain global optimum solutions. Recently, Symbiotic Organisms Search (SOS) has been applied to various optimization problems and results obtained were found to be competitive with state-of-the-art metaheuristic algorithms. However, similar to the case other metaheuristic optimization algorithms, the efficiency of SOS algorithm deteriorates as the size of the search space increases. Moreover, SOS suffers from local optima entrapment and its static control parameters cannot maintain a balance between local and global search. In this study, Cooperative Coevolutionary Constrained Multiobjective Symbiotic Organisms Search (CC-CMSOS), Cooperative Coevolutionary Constrained Multi-objective Memetic Symbiotic Organisms Search (CC-CMMSOS), and Cooperative Coevolutionary Constrained Multi-objective Adaptive Benefit Factor Symbiotic Organisms Search (CC-CMABFSOS) algorithms are proposed to solve constrained multi-objective large scale task scheduling optimization problem on IaaS cloud computing environment. To address the issue of scalability, the concept of Cooperative Coevolutionary for enhancing SOS named CC-CMSOS make SOS more efficient for solving large scale task scheduling problems. CC-CMMSOS algorithm further improves the performance of SOS algorithm by hybridizing with Simulated Annealing (SA) to avoid entrapment in local optima for global convergence. Finally, CC-CMABFSOS algorithm adaptively turn SOS control parameters to balance the local and global search procedure for faster convergence speed. The performance of the proposed CC-CMSOS, CC-CMMSOS, and CC-CMABFSOS algorithms are evaluated on CloudSim simulator, using both standard workload traces and synthesized workloads for larger problem instances of up to 5000. Moreover, CC-CMSOS, CC-CMMSOS, and CC-CMABFSOS algorithms are compared with multi-objective optimization algorithms, namely, EMS-C, ECMSMOO, and BOGA. The CC-CMSOS, CC-CMMSOS, and CC-CMABFSOS algorithms obtained significant improved optimal trade-offs between execution time (makespan) and financial cost (cost) while meeting deadline constraints with no computational overhead. The performance improvements obtained by the proposed algorithms in terms of hypervolume ranges from 8.72% to 37.95% across the workloads. Therefore, the proposed algorithms have potentials to improve the performance of QoS delivery

Evolutionary multi-objective decision support systems for conceptual design

Merged with duplicate record 10026.1/2328 on 07.20.2017 by CS (TIS)In this thesis the problem of conceptual engineering design and the possible use of adaptive search techniques and other machine based methods therein are explored. For the multi-objective optimisation (MOO) within conceptual design problem, genetic algorithms (GA) adapted to MOO are used and various techniques explored: weighted sums, lexicographic order, Pareto method with and without ranking, VEGA-like approaches etc. Large number of runs are performed for findingZ Dth e optimal configuration and setting of the GA parameters. A novel method, weighted Pareto method is introduced and applied to a real-world optimisation problem. Decision support methods within conceptual engineering design framework are discussed and a new preference method developed. The preference method for translating vague qualitative categories (such as "more important 91 , 4m.9u ch less important' 'etc. ) into quantitative values (numbers) is based on fuzzy preferences and graph theory methods. Several applications of preferences are presented and discussed: * in weighted sum based optimisation methods; s in weighted Pareto method; * for ordering and manipulating constraints and scenarios; e for a co-evolutionary, distributive GA-based MOO method; The issue of complexity and sensitivity is addressed as well as potential generalisations of presented preference methods. Interactive dynamical constraints in the form of design scenarios are introduced. These are based on a propositional logic and a fairly rich mathematical language. They can be added, deleted and modified on-line during the design session without need for recompiling the code. The use of machine-based agents in conceptual design process is investigated. They are classified into several different categories (e. g. interface agents, search agents, information agents). Several different categories of agents performing various specialised task are developed (mostly dealing with preferences, but also some filtering ones). They are integrated with the conceptual engineering design system to form a closed loop system that includes both computer and designer. All thesed ifferent aspectso f conceptuale ngineeringd esigna re applied within Plymouth Engineering Design Centre / British Aerospace conceptual airframe design project.British Aerospace Systems, Warto