A Novel Multi-Task Optimization Algorithm Based on the Brainstorming Process

Abstract

Evolutionary multi-task optimization (EMTO) is an emerging research topic in the field of evolutionary computation, which aims to simultaneously optimize several component tasks within a problem and output the best solution for each task. Since EMTO has widespread applications in solving real-world multi-task optimization problems, in recent years, some EMTO algorithms have been proposed. However, most of which are based on the multifactorial evolution framework which has difficulties in independently controlling the optimization of each component task and implementing parallel computing. To tackle this problem and enrich the EMTO algorithms' family, this paper firstly designs a novel EMTO framework inspired by the brainstorming process of human beings when they solve multi-task problems. Under this framework, a novel EMTO algorithm, named as brain storm multi-task optimization (BSMTO), is presented, where the optimization for each component task and the knowledge transfer between different tasks are both implemented by the proposed brainstorming operations. Furthermore, through investigating the knowledge transfer process in the proposed algorithm, an enhanced BSMTO algorithm named as BSMTO-II is further proposed, where the knowledge transfer in each component task can be managed and controlled by our newly designed scheme. Finally, the proposed two algorithms are tested on benchmark problems. Experimental results show that BSMTO-II has a competitive performance compared with both classical and state-of-the-art algorithms. Moreover, the effectiveness of the proposed EMTO framework and the knowledge transfer control scheme is proved through experiments, and the key parameters settings and the algorithmic complexity are also discussed at last.

41 st International Conference on Organizational Science Development

“…Omenjena faza je zaradi visoke stopnje razvitosti obdelave naravnega jezika primerna za integracijo v programskem orodju. Zanimivo je, da se principi »brainstorminga« prenašajo tudi na področje umetne inteligence (Lyu et al, 2020).…”

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

“…Based on the saturation point of the knowledge transfer (SPKT), the knowledge transfer control scheme was proposed to control the generation of hybrid-offspring and alleviate the harmful transferred knowledge [99]. Based on the efficiencies of the global search and local search component, Liu et al [86] proposed an adaptive control strategy, which can determine whether to perform the global search (DE) or the local search (CMA-ES) during the evolution.…”

“…In addition, two DE-specific knowledge reuse strategies were also studied in [132]: the base vector based strategy and the differential vector based strategy. Inspired by the cluster-based search feature of brain storm optimization (BSO), a brain storm multi-task problems solver (BSMTPS) framework was proposed by dividing individuals into several groups [99]. As illustrated in Figure 11, the offspring are generated by the internal brain storm (IBS) and the cross-task brain storm (CBS), achieving knowledge transfer within a special task and across different tasks, respectively.…”

“…Since the first establishment of MFEA, a number of MTEC algorithms have been proposed and successfully applied in many benchmark problems and real-world problems over the past few years, as summarized in Table 3. MFEA [11], MFEA [18], None [21], MFEA-GHS [22], G-MFEA [39], MFEA-II [41], ASCMFDE [42], PGEA [49], MFDE with AIM [50], MPEF-SHADE [82], MFMP [83], MFDE [85], MFPSO [85], SaM-MA [86], MT-CPSO [86], MDE-DVSM [87], MTMSO [90], CPSOM [92], AMFPSO [94], MTO-FWA [96], MFBSO [98], BSMTO [99], BSMTO-II [99], EMTSO-CCMA [101], MFEARR [104], DEMTO [105], MFEA-DV [110], EMT-RE [111], LDA-MFEA [113],…”

“…Specifically, several typical genetic strategies include simulated binary crossover [18,79], ordered crossover (OX) [57,80], onepoint crossover [59,61], DE crossover [61], guided differential evolutionary crossover [81], partially mapped crossover (PMX) and two-point crossover (TPX) [71], Gaussian mutation [18], uniform mutation [61], swap mutation (SW) [57,80], polynomial mutation [53,79], DE mutation [61], mutation using the Powell search method [81], swap-change mutation [64], and one-point mutation [71]. The other EAs, differential evolution (DE) [82][83][84][85][86][87], particle swarm optimization (PSO) [85][86][87][88][89][90][91][92][93][94], artificial bee colony (ABC) [95], fireworks algorithm (FWA) [96], self-organized migrating algorithm (SOMA) [97], brain storm optimization (BSO) [98,99], Bat Algorithm (BA) [100], and genetic programming (GP) [61], are also utilized as fundamental algorithm for MTEC paradigms.…”