**Preface to "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 obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms should try to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Many researchers have made different findings on the study of intelligent optimization algorithms.

In terms of improving algorithm performanc, Li et al. proposed a co-evolutionary algorithm based on a dynamic learning strategy. The evolution is mainly achieved by using the Pareto criterion and the non-Pareto criterion for two populations, respectively, and using the information exchange between the two populations to better explore the whole target space. Hussien et al. proposed two binary variants of the Whale Optimization Algorithm (WOA), called bWOA-S and bWOA-V. They are used to reduce the complexity and improve the performance of the system by selecting important features for classification purposes. Sun et al. proposed a steering-based variational approach for solving the premature convergence problem of the success history-based adaptive differential evolution algorithm in a high-dimensional search space. Wei et al. optimized the particle swarm optimization algorithm by quantum behavior and optimized the krill swarm algorithm by simulated annealing, thus proposing a new hybrid algorithm called the annealed krill quantum particle swarm optimization (AKQPSO) algorithm. A comprehensive review of algorithms based on elephant grazing optimization and their applications is presented by Li et al. Various aspects of EHO variants for continuous optimization, combinatorial optimization, constrained optimization, and multi-objective optimization are reviewed. Future research directions in the field of EHO are further discussed. Novak et al. proposed a new metric for game feature verification in real-time strategy (RTS) games, comparing evolutionary and tree-based approaches for game feature verification in real-time strategy games.

In order to solve complex problems. Li et al. proposed a discrete artificial bee colony (DABC) algorithm based on similarity and non-dominated solution ordering, which can solve the fuzzy hybrid green shop scheduling problem with fuzzy processing time. Li et al. proposed a new modal strategy based on particle swarm optimization algorithm, which can solve the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion. Zhao et al. proposed a novel multi-objective optimization solution, MooFuzz, which identifies different states of the seed pool and continuously collects different information about the seeds to guide seed scheduling and energy allocation. The method can be used to find bugs and vulnerabilities in software. Wang et al. proposed a new quantum-inspired differential evolution algorithm based on the gray wolf optimizer, which can solve the 0-1 backpacking problem. Zhang et al. proposed a pair-wise ant colony optimization algorithm combined in position-based learning in order to solve the traveling merchant problem (TSP). Two strategies for constructing opposite paths based on TSP solution features for OBL were also proposed. Muhammad et al. conducted a comparative study of multi-objective evolutionary algorithms and single-objective evolutionary algorithms in optimizing the knapsack problem (KNP) and the traveling merchant problem (TSP). Marrero et al. proposed a multi-objective modal approach based on the decomposition-based multi-objective evolutionary algorithm (MOEA/D). The method contains crossover operators specifically designed for this problem. In addition, an interim iterative local search (ILS) is considered in the improvement phase. The modified algorithm can be used to develop healthy, balanced and inexpensive menu plans. Rahnamayan et al. proposed a new and

improved Pareto dominance depth ranking strategy that uses some dominance indicators obtained from the basic Pareto dominance depth ranking and some ranked statistical indicators to rank scientific outcomes. Li et al. proposed a new CS extension with Q-learning steps and genetic operators, namely the dynamic step cuckoo search algorithm (DMQL-CS), which can be used to solve the logistics distribution center site selection problem.

The editors are confident that this book will help beginners to understand the principles and design of intelligent algorithms. This book serves as a viable resource for readers interested in the applications of intelligent algorithms. It will further promote the development and improvement of intelligent algorithm research, strengthen the research of computational intelligence algorithms, and promote the intersection and integration of related disciplines.

> **Gai-Ge Wang, Amir H. Alavi** *Editors*

#### *Review* **Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review**

**Qingzheng Xu 1,2, Na Wang 1, Lei Wang 3,\*, Wei Li 4 and Qian Sun 4**

> 4

	- School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China;15691751097@163.com (Q.S.)

**Abstract:** Traditional evolution algorithms tend to start the search from scratch. However, real-world problems seldom exist in isolation and humans effectively manage and execute multiple tasks at the same time. Inspired by this concept, the paradigm of multi-task evolutionary computation (MTEC) has recently emerged as an effective means of facilitating implicit or explicit knowledge transfer across optimization tasks, thereby potentially accelerating convergence and improving the quality of solutions for multi-task optimization problems. An increasing number of works have thus been proposed since 2016. The authors collect the abundant specialized literature related to this novel optimization paradigm that was published in the past five years. The quantity of papers, the nationality of authors, and the important professional publications are analyzed by a statistical method. As a survey on state-of-the-art of research on this topic, this review article covers basic concepts, theoretical foundation, basic implementation approaches of MTEC, related extension issues of MTEC, and typical application fields in science and engineering. In particular, several approaches of chromosome encoding and decoding, intro-population reproduction, interpopulation reproduction, and evaluation and selection are reviewed when developing an effective MTEC algorithm. A number of open challenges to date, along with promising directions that can be undertaken to help move it forward in the future, are also discussed according to the current state. The principal purpose is to provide a comprehensive review and examination of MTEC for researchers in this community, as well as promote more practitioners working in the related fields to be involved in this fascinating territory.

**Keywords:** multi-task optimization; multi-task evolutionary computation; knowledge transfer; evolutionary algorithm; assortative mating; unified search space
