本文為英國埃克塞特大學（作者：Kevin Ian Smith）的電腦科學博士論文，共137頁。

許多應用領域的計算優化問題都歸結為多目標優化問題，從而達到最小化或最大化的目的。如果（通常情況下）這些目標都是相互競爭的目標，則優化的目的是找出一組合適的解決方案，當沒有關於目標的進一步偏好資訊時，這些解決方案的質量無法區分。已有大量文獻記載了進化演算法在多目標優化中的研究和應用，其中特別著重於進化策略技術，這些技術表現出在許多問題上快速收斂到期望解的能力。

模擬退火是一種單目標優化技術，具有可證明的收斂性，使其成為向多目標優化擴充套件的誘人技術。已有研究關於將模擬退火擴充套件到多目標情況的建議大多采用優化目標的複合(求和形式)函式的傳統單目標模擬退火器的形式。論文的第一部分介紹了一種多目標模擬退火的替代方法，用於處理不需要給目標分配偏好資訊的優化演算法。對這種演算法提出了非通用的改進，為產生更理想的新解決方案建議提供了方法。根據問題的性質，這種新方法顯示出對期望集的快速收斂性，與一般NSGA-II遺傳演算法和領先的多目標模擬退火演算法相比，該方法對一系列常見測試問題具有經驗結果。將該新演算法應用於CDMA行動通訊網路的商業優化，結果表明該演算法具有良好的效能。

本文的第二部分包含了對一系列優化器效能收斂影響的研究，提出了新的演算法，並與所需要的特性進行了分析調查。闡明瞭進化策略與模擬退火技術之間的關係，並解釋了先前提出的演算法在標準測試樣本上的不同效能。本文研究了模擬退火方法所要解決問題的特性，並提出了新問題，以便最好地比較不同的模擬退火技術。

Many areas in which computational optimisation may be applied aremulti-objective optimization problems; those where multiple objectives must beminimised (for minimisation problems) or maximised (for maximisation problems).Where (as is usually the case) these are competing objectives, the optimisationinvolves the discovery of a set of solutions the quality of which cannot be distinguishedwithout further preference information regarding the objectives. A large bodyof literature exists documenting the study and application of evolutionaryalgorithms to multi-objective optimisation, with particular focus being givento evolutionary strategy techniques which demonstrate the ability to convergeto desired solutions rapidly on many problems.

Simulated annealing is a single-objective optimisationtechnique which is provably convergent, making it a tempting technique forextension to multi-objective optimisation. Previous proposals for extendingsimulated annealing to the multi-objective case have mostly taken the form of atraditional single-objective simulated annealer optimising a composite (oftensummed) function of the objectives. The first part of this thesis deals withintroducing an alternate method for multiobjective simulated annealing, dealingwith the dominance relation which operates without assigning preferenceinformation to the objectives. Non-generic improvements to this algorithm arepresented, providing methods for generating more desirable suggestions for newsolutions. This new method is shown to exhibit rapid convergence to the desiredset, dependent upon the properties of the problem, with empirical results on arange of popular test problems with comparison to the popular NSGA-II geneticalgorithm and a leading multi-objective simulated annealer from the literature.The new algorithm is applied to the commercial optimisation of CDMA mobiletelecommunication networks and is shown to perform well upon this problem.

The second section of this thesis contains aninvestigation into the effects upon convergence of a range of optimiserproperties. New algorithms are proposed with the properties desired to investigate.The relationship between evolutionary strategies and the simulated annealingtechniques is illustrated, and explanation of the differing performance of thepreviously proposed algorithms across a standard test suite is given. Theproperties of problems on which simulated annealer approaches are desirable areinvestigated and new problems proposed to best provide comparisons betweendifferent simulated annealing techniques.

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