A survey on evolutionary computation for complex continuous optimization

Abstract

Complex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including Many-dimensions, Many-changes, Many-optima, Many-constraints, and Many-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including reducing problem difficulty, increasing algorithm diversity, accelerating convergence speed, reducing running time, and extending application field. Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.

Prediction of silicon content in the hot metal using Bayesian networks and probabilistic reasoning

“…An increase in the enrichment content of O2 tends to increase the temperature of the blast furnace and decrease the amount of nitrogen injected, thereby increasing the thermal level and favoring the permeability of the blast furnace. Thus, a high or thicker cohesive zone tends to increase the silicon content in the hot metal as well as a higher thermal level which favors the conditions for the incorporation of silicon into the hot metal, which may indeed be influenced by variables [49]- [51].…”

Many-Objective Job-Shop Scheduling: A Multiple Populations for Multiple Objectives-Based Genetic Algorithm Approach

“…In the future, we will focus on other types of JSSPs (e.g., flexible JSSPs [49], [50], flow shop scheduling problems [51]- [53]), and extend the MPMO framework with other evolutionary computation [54] (e.g., particle swarm optimization [55]- [57], differential evolution [58]- [60], estimation of distribution algorithm [61], and gravitational search algorithm [62]) for efficiently solving them. Besides, the incorporation with distributed computing technique [63]- [65] or matrix-based technique [66] will be further studied to reduce the running time of the algorithm.…”

A Meta-Knowledge Transfer-Based Differential Evolution for Multitask Optimization

“…For future work, the proposed algorithm will be further extended to solving more difficult and complex MTOPs, such as not only in complex continuous space [54]- [56], but also in complex discrete [57]- [60], combinational [61]- [64], and mix-variable space [65]- [67]. Furthermore, as the MKT is a generic idea, further exploration of other kinds of meta-knowledge and other meta-knowledge transfer methods and utilization methods are worthy studied to obtain more powerful EMTO algorithms.…”