Optimization algorithms, benchmarks and performance measures: From static to dynamic environment

Abstract

This paper is a tentative to describe the basics of dynamic optimization using swarm & evolutionary methods. Computational intelligence methods based on swarming, collaborative computing and related techniques showed their potentials at solving classical static problems; for dynamic problems new paradigms needs to be established, this concerns the methods, the test benches and the performance evaluation processes. A review of the key population based computational techniques is performed prior to set some perspective guidelines on how to handle the multi-objective dynamic problems using these technique.

Multi Objective Particle Swarm Optimization Based Cooperative Agents with Automated Negotiation

“…A new version of MOPSO [18] adopts the Pareto ranking to dynamic subdivide the population. There are a few variants of hierarchical architecture are proposed in [19][20][21] that is proposed by Fdhila. Its main idea is to have a 2-levels that adopts a bidirectional dynamic exchange of particles between MOPSOs.…”

Dynamic Multi Objective Particle Swarm Optimization Based on a New Environment Change Detection Strategy

“…In the literature, dynamism tasks are related to the objective function, constraints and the parameters of a predefined problem that change over the time. Dynamic multiobjective problems pose big challenges associated with the evolutionary computation approaches [17][18][19][20]. Hence, the PSO methods are proven as a good technique to solve a single and multi-objective problem in a static environment.…”