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Multi‐Objective Evolutionary Algorithms and Evolutionary Large‐Scale Optimization

1.1 Introduction

The word optimization initially appeared in the mid‐19th century, meaning “to make the best or most of.” Today, optimization has deeply penetrated into our thoughts and actions: engineers aim to achieve the best performance of their designs; businessmen seek to maximize the profits of their deals; manufacturers expect the highest efficiency of their production processes; investors try to minimize the risk of investment while maximizing rate of return, etc. With sophisticated formulations and modelings, such optimization‐driven targets become optimization problems.

With problems always come solvers, as known as algorithms in the context of optimization. In the early age, the conventional optimization algorithms were often gradient based, e.g., Newton's method and the hill climbing method, assuming that the problems to be solved were convex (or at least differentiable) – and indeed – problems were supposed to be well defined by mathematicians. Nonetheless, the information age commencing from the mid‐20th century has led modernized science, engineering, industry, and modernized optimization as well. While enjoying the benefits of modernization, we are facing emerging challenges – optimization problems are increasingly complex, far beyond the scope of conventional optimization algorithms.

Figure 1.1 The generic flowchart of an Evolutionary Algorithm (EA).

Scientists have always been dedicated to looking into nature to find answers to research questions, so as in the case of solving optimization problems. The nature‐inspired optimization algorithms are exactly a family of modern algorithms for global optimization. Facing the challenging features of complex optimization problems (e.g., nonlinearity, non‐differentiability, or multi‐modality), nature‐inspired optimization algorithms benefit from unique advantage of robustness – treating optimization problems as black‐boxes. Particularly, inspired by the mechanisms of biological evolution, the evolutionary algorithms (EAs) [1] are among the most representative niches of the family. Generally speaking, EAs encompass genetic algorithms (GAs), evolutionary strategies, evolutionary programming, and genetic programming.

As illustrated in Fig. 1.1, a generic flowchart of EAs comprises five main components: initialization, mating selection, variation, environmental selection, and termination. Normally, an EA starts with a randomly initialization population of candidate solutions. Then, the population of candidate solutions is iteratively updated via the main loop consisting of mating selection, variation, and environmental selection. Specifically, mating selection randomly picks up pairwise (parent) candidate solutions for generating new (offspring) candidate solutions via variation, and environmental selections decide which candidate solutions can survive to the next iteration (generation). Eventually, when a certain condition (e.g., the maximum iteration number) is satisfied, the EA will terminate and output the candidate solutions in the final population as the approximate optimal solution(s) to the optimization problem being solved.

During the past three decades, EAs have achieved remarkable success in solving complex optimization problems; recently, however, they are facing a new challenge brought by the surging development of information sciences – the scalability. Just as many other computer algorithms, the nature‐inspired optimization algorithms suffer from a serious curse of dimensionality, i.e., as the scale of the optimization problems increases, the performance of the algorithms substantially deteriorates. Now, we are stepping forward into new adventures – scaling up EAs for solving large‐scale optimization problems.

Before moving toward the main course of large‐scale optimization problems, we might start with an appetizer – general optimization problems. Generally, an optimization problem often consists of three essential components: objective(s), decision variable(s), and constraint(s): an objective depicts the performance of the system under study, and a decision variable is a certain characteristic in correlation with the objective(s); the spaces where the objectives and decision variables are defined are often known as objective space and decision space, respectively, and a constraint specifies the limitation(s) in either the objective space or decision space. Correspondingly, an optimization problem can be mathematically formulated as1:

where is the decision space and is the decision vector; is the objective space and is the objective vector. The decision vector is composed of decision variables while the objective vector is composed of objective functions that map from to .

Considering the number of objectives, optimization problems can be intrinsically categorized into two types: single‐objective optimization problems (SOPs) (i.e., ) and multi‐objective optimization problems (MOPs) (i.e., ). For an SOP, the optimal solution is often a single decision vector minimizing (or maximizing) the objective. For an MOP, since the multiple objectives are often conflicting with each other, it is impossible to find any single solution minimizing (or maximizing) all objectives simultaneously; instead, the optima to an MOP is often a set of solutions trading‐off between different objectives, known as the Pareto optimal solutions. Specially, an MOP with more than three objectives is also known as a many‐objective optimization problem (MaOP) due to its particular challenges [2].

Figure 1.2 This figure illustrates the classification of large optimization problems involved in this chapter. Generally, the problems can have either a large number of decision variables or many objectives, which can be further categorized into four classes: single‐objective problems with a large number of decision variables (LSSOPs), multi‐objective optimization problems with a large number of decision variables (LSMOPs), many‐objective optimization problems (MaOPs), and many‐objective optimization problems with a large number of decision variables (LSMaOPs).

As illustrated in Fig. 1.2, optimization problems can be large scale in terms of either objectives or decision variables, intrinsically comprising four types: (i) SOPs with a large number of decision variables (LSSOPs), (ii) MOPs with a large number of decision variables (LSMOPs), (iii) MaOPs with a normal scale of decision variables, and (iv) MaOPs with a large number of decision variables (LSMaOPs).

For SOPs, the optimization target is quite straightforward – searching for the global optimum solution which minimizes the objective function . By contrast, for MOPs/MaOPs, due to the possible conflicts between different objectives, no single solution is able to minimize all objectives simultaneously. As a consequence, there exist a set of optimal solutions trading‐off between different objectives, namely, the Pareto optimality – defining a situation where no solution can be better off without making at least another solution worse off. In other words, the optimality of MOPs/MaOPs is defined upon the pairwise relationship (the dominance relationship) among candidate solutions considering the trade‐offs between different optimization objectives. To be specific, a solution is said to dominate another solution (denoted as ) iff

If a solution cannot be dominated by any other feasible solutions, is said to be Pareto optimal and the union of all is called Pareto set (PS), while the union of is termed Pareto front (PF). An illustrative example of Pareto optimality is given in Fig. 1.3.

Figure 1.3 An illustrative example to Pareto optimality. The images of a number of candidate solutions (, , , etc.) are spreading in the objective space. and , non‐dominated to each other, are among the Pareto optimal solutions on the PF (PS). is dominated by both and .

For most continuous MOPs/MaOPs, the PS (PF) often consists of an infinite number of Pareto optimal solutions. Therefore, in practice, only a representative subset of the PS (PF) can be approximated using a limited number of non‐dominated solutions, which are often the candidate solutions not dominated by any other in the final solution set obtained by an algorithm.

In this chapter, we mainly focus on problems of type (ii) and type (iv), i.e., LSMOPs and LSMaOPs. Since MaOPs are essentially MOPs of a special case, for simplicity, we unified the abbreviations of both LSMOPs and LSMaoPs to be LSMOPs. Specifically, we refer to research adopting EAs to solve large‐scale optimization problems as evolutionary large‐scale optimization. The rest of this chapter is organized as follows: Section 1.2 briefly introduces multi‐objective evolutionary algorithms (MOEAs), Section...