A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Abstract

Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

Adjustability of a discrete particle swarm optimization for the dynamic TSP

“…The old solutions in the memory were used as starting points when a change to the problem definition was detected, as the changes could result in a new optimum being in the vicinity of the old one. A recent thorough survey on swarm intelligence methods for solving the dynamic COPs (continuous and discrete) can be found in Mavrovouniotis et al (2017).…”

Frameworks for Performing on Cloud Automated Software Testing Using Swarm Intelligence Algorithm: Brief Survey

“…For example, in April 24 1994, a China airline airbus A-300 crashed, due to a software bug, resulting the death of 264 innocent lives [2,3]. Another famous incident was reported in April 1999, where a small software bug in a military satellite was behind a $1.2 billion loss: one of the costliest unmanned accidents in the history of Cape Canaveral launches [4,5]. Therefore, to reduce the risks of errors, researchers have developed a variety of testing techniques to find and fix software bug early and before the deployment of the software.…”

Comparative Study of Type-2 Fuzzy Particle Swarm, Bee Colony and Bat Algorithms in Optimization of Fuzzy Controllers

“…At present the works related to the dynamic adjustment of parameters in BCO are: in [35], a Sets in Dynamic Adaptation of Parameters of a Bee Colony Optimization for Controlling the Trajectory of an Autonomous Mobile Robot is shown; in [36], an improved artificial bee colony algorithm for solving constrained optimization problems is presented; in [37], a survey of swarm intelligence for dynamic optimization: algorithms and applications is shown; and, in [21], an Efficient cooperative relaying in flying ad hoc networks using fuzzy-bee colony optimization is presented, among others.…”