Matrix-Based Evolutionary Computation

Abstract

Computational intelligence (CI), including artificial neural network, fuzzy logic, and evolutionary computation (EC), has rapidly developed nowadays. Especially, EC is a kind of algorithm for knowledge creation and problem solving, playing a significant role in CI and artificial intelligence (AI). However, traditional EC algorithms have faced great challenge of heavy computational burden and long running time in large-scale (e.g., with many variables) problems. How to efficiently extend EC algorithms to solve complex problems has become one of the most significant research topics in CI and AI communities. To this aim, this paper proposes a matrix-based EC (MEC) framework to extend traditional EC algorithms for efficiently solving large-scale or super large-scale optimization problems. The proposed framework is an entirely new perspective on EC algorithm, from the solution representation to the evolutionary operators. In this framework, the whole population (containing a set of individuals) is defined as a matrix, where a row stands for an individual and a column stands for a dimension (decision variable). This way, the parallel computing functionalities of matrix can be directly and easily carried out on the high performance computing resources to accelerate the computational speed of evolutionary operators. This paper gives two typical examples of MEC algorithms, named matrix-based genetic algorithm and matrix-based particle swarm optimization. Their matrix-based solution representations are presented and the evolutionary operators based on the matrix are described. Moreover, the time complexity is analyzed and the experiments are conducted to show that these MEC algorithms are efficient in reducing the computational time on large scale of decision variables. The MEC is a promising way to extend EC to complex optimization problems

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. Her research interests mainly include multiobjective optimization, many-objective optimization, and their applications in real-world problems.…”

A survey on evolutionary computation for complex continuous optimization

“…This way, a trade-off can be achieved between the exploration ability and the exploitation ability to better solve complex continuous optimization problems. Very recently, the new pipeline-based parallel technique for EC (Li et al 2020b) and matrix-based EC (MEC) (Zhan et al 2021) have been proposed, and are worthy for future study in solving complex continuous optimization problems, especially the LSOP.…”

A Novel Three-Dimensional Path Planning Method for Fixed-Wing UAV Using Improved Particle Swarm Optimization Algorithm

“…The length of the path with good performance is considered within the scope of 1.5 times the distance between start and destination according to [15]. Hence, the value of 1.5 is the preference of feasible PLR.…”

“…The problem of trajectory planning is often treated as a nonlinear NP-hard optimal problem to be solved by evolutionary algorithms. It is well known that the ECs have made much progress and have been deeply and extensively studied in recent years [14][15][16]. EC algorithms, including evolutionary algorithms (EAs) and swarm intelligence algorithms (SIs), such as genetic algorithm (GA) [17], differential evolution (DE) [18,19], particle swam optimization (PSO) [20][21][22][23][24][25], and ant colony optimization (ACO) [26], have been widely used in handling global optimization problems.…”