3 research outputs found
Multifactorial Cellular Genetic Algorithm (MFCGA): Algorithmic Design, Performance Comparison and Genetic Transferability Analysis
Multitasking optimization is an incipient research area which is lately
gaining a notable research momentum. Unlike traditional optimization paradigm
that focuses on solving a single task at a time, multitasking addresses how
multiple optimization problems can be tackled simultaneously by performing a
single search process. The main objective to achieve this goal efficiently is
to exploit synergies between the problems (tasks) to be optimized, helping each
other via knowledge transfer (thereby being referred to as Transfer
Optimization). Furthermore, the equally recent concept of Evolutionary
Multitasking (EM) refers to multitasking environments adopting concepts from
Evolutionary Computation as their inspiration for the simultaneous solving of
the problems under consideration. As such, EM approaches such as the
Multifactorial Evolutionary Algorithm (MFEA) has shown a remarkable success
when dealing with multiple discrete, continuous, single-, and/or
multi-objective optimization problems. In this work we propose a novel
algorithmic scheme for Multifactorial Optimization scenarios - the
Multifactorial Cellular Genetic Algorithm (MFCGA) - that hinges on concepts
from Cellular Automata to implement mechanisms for exchanging knowledge among
problems. We conduct an extensive performance analysis of the proposed MFCGA
and compare it to the canonical MFEA under the same algorithmic conditions and
over 15 different multitasking setups (encompassing different reference
instances of the discrete Traveling Salesman Problem). A further contribution
of this analysis beyond performance benchmarking is a quantitative examination
of the genetic transferability among the problem instances, eliciting an
empirical demonstration of the synergies emerged between the different
optimization tasks along the MFCGA search process.Comment: Accepted for its presentation at WCCI 202
A Two stage Adaptive Knowledge Transfer Evolutionary Multi-tasking Based on Population Distribution for Multi/Many-Objective Optimization
Multi-tasking optimization can usually achieve better performance than
traditional single-tasking optimization through knowledge transfer between
tasks. However, current multi-tasking optimization algorithms have some
deficiencies. For high similarity problems, the knowledge that can accelerate
the convergence rate of tasks has not been fully taken advantages of. For low
similarity problems, the probability of generating negative transfer is high,
which may result in optimization performance degradation. In addition, some
knowledge transfer methods proposed previously do not fully consider how to
deal with the situation in which the population falls into local optimum. To
solve these issues, a two-stage adaptive knowledge transfer evolutionary
multi-tasking optimization algorithm based on population distribution, labeled
as EMT-PD, is proposed. EMT-PD can accelerate and improve the convergence
performance of tasks based on the knowledge extracted from the probability
model that reflects the search trend of the whole population. At the first
transfer stage, an adaptive weight is used to adjust the step size of
individual's search, which can reduce the impact of negative transfer. At the
second stage of knowledge transfer, the individual's search range is further
adjusted dynamically, which can improve the diversity of population and be
beneficial for jumping out of local optimum. Experimental results on
multi-tasking multi-objective optimization test suites show that EMT-PD is
superior to other six state-of-the-art evolutionary multi/single-tasking
algorithms. To further investigate the effectiveness of EMT-PD on
many-objective optimization problems, a multi-tasking many-objective test suite
is also designed in this paper. The experimental results on the new test suite
also demonstrate the competitiveness of EMT-PD.Comment: 14 pages, 8 figures, 7 tables, 61 reference
AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking
Transfer Optimization is an incipient research area dedicated to the
simultaneous solving of multiple optimization tasks. Among the different
approaches that can address this problem effectively, Evolutionary Multitasking
resorts to concepts from Evolutionary Computation to solve multiple problems
within a single search process. In this paper we introduce a novel adaptive
metaheuristic algorithm for dealing with Evolutionary Multitasking environments
coined as Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm
(AT-MFCGA). AT-MFCGA relies on cellular automata to implement mechanisms for
exchanging knowledge among the optimization problems under consideration.
Furthermore, our approach is able to explain by itself the synergies among
tasks that were encountered and exploited during the search, which helps
understand interactions between related optimization tasks. A comprehensive
experimental setup is designed for assessing and comparing the performance of
AT-MFCGA to that of other renowned evolutionary multitasking alternatives (MFEA
and MFEA-II). Experiments comprise 11 multitasking scenarios composed by 20
instances of 4 combinatorial optimization problems, yielding the largest
discrete multitasking environment solved to date. Results are conclusive in
regards to the superior quality of solutions provided by AT-MFCGA with respect
to the rest of methods, which are complemented by a quantitative examination of
the genetic transferability among tasks along the search process.Comment: 30 pages, 2 figures, under review for its consideration in
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