## 1 Introduction  
Evolutionary algorithms (EAs) are population-based stochastic optimization methods that include mechanisms of natural selection and population genetics in the field of Artificial Intelligence [1; 2; 3]. They are based on a collective learning process and can start with an arbitrarily initialized population [4; 5]. Individuals evolve towards better and better solution regions by means of repetitive reproduction and mutation [6; 7; 8; 9]. Due to their flexibility, ease of interfacing, extensibility, and high search efficiency, population-based optimization has been successfully applied to path planning, task assignment, energy-saving, and network configuration [10; 11; 12; 13; 14; 15; 16]. Even though EAs are proven to be useful in many applications, they have some disadvantages. For example, it is known that there are many similar optimization problems in the real world. Traditional EAs just focus on solving a single optimization problem and regard each optimization as an independent process while ignoring similarities among different tasks. When handling a new optimization problem, EAs have to start from scratch by assuming that no historical knowledge of solving related problems can be used [17]. Inspired by the idea of transfer learning and multi-task learning that a system can learn knowledge or skills from performing previous tasks [18; 19; 20], this work aims to solve multiple optimization problems and explore their implicit facilitatings. Traditionally, optimization problems can be divided into single-objective optimization (SOO) and multi-objective one (MOO). In order to improve the search efficiency among related tasks, a new paradigm named multi-task optimization is proposed. Instead of solving multiple problems sequentially, MTO can utilize similarities of different tasks to improve search efficiencies [21; 22; 23]. Inspired by multifactorial inheritance, [PERSON] et al. proposed a novel genetic EA named multifactorial evolutionary algorithm (MFEA) to solve MTO problems [21]. In MFEA, complex developmental traits of offspring are influenced by the combination of genetic and cultural factors [24; 25; 26]. During the evolution, MFEA can utilize information transfer of similar problems to improve optimization performance, which differs from traditional single-task methods [27; 28; 29; 30]. Many MFEA variants have been proposed [31; 32]. For example, in order to explore the generality of multifactorial optimization (MFO) when using different population-based search mechanisms, [PERSON] et al. [33] attempted to conduct MFO with particle swarm optimization (PSO) and differential evolution (DE). The derived two multi-tasking paradigms are named as multifactorial particle swarm optimization (MFPSO) and multifactorial differential evolution (MFDE). [PERSON] et al. [17] proposed a general evolutionary framework named MaTEA for solving many-task optimization problems. It chose assisted tasks based on the Kullback-Leibler divergence (KLD) mechanism that can be used to measure the similarity among different tasks. With a strong motivation for improving optimization efficiency and choosing suitable individuals to learn from other tasks, we propose an individually guided multi-task optimization framework. It is capable of generalizing almost all existing single-objective solvers, and answers when to start transfer and who should learn. In this paper, we place four popular single-objective solvers, including genetic algorithm (GA), PSO, DE, and artificial bee colony (ABC) into our proposed framework to validate its usefulness in advancing the field of MTO optimization. We intend to make the following novel contributions:  
1. We propose a novel MTO framework including the partial population information sharing and individual learning schemes to achieve higher search efficiency than existing frameworks. 2. In order to represent the interests of each individual for solving different tasks, we introduce a new concept of skill membership into the MTO framework. 3.