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In electric power systems, finding optimal location and setting of flexible AC transmission system (FACTS) devices represents a difficult optimisation problem. This is due to its discrete, multi-objective, multi-modal and constrained nature. Finding near-global solutions in such a problem is very demanding. Brain-storm optimisation algorithm (BSOA) is a(More)
Big bang–big crunch (BBBC) algorithm is a fairly novel gradient-free optimisation algorithm. It is based on theories of evolution of the universe, namely the big bang and big crunch theory. The big challenge in BBBC is that it is easily trapped in local optima. In this paper, chaotic-based strategies are incorporated into BBBC to tackle this challenge. Five(More)
In many optimisation problems, all or some of decision variables are discrete. Solving such problems are more challenging than those problems with pure continuous variables. Among various optimisation techniques, particle swarm optimisation (PSO) has demonstrated more promising performance in tackling discrete optimisation problems. In PSO, basic variants(More)
Almost all real-world optimisation problems are constrained. Solving constrained problems is difficult for optimisation techniques. In this paper, different constraint handling strategies used in heuristic optimisation algorithms and especially particle swarm optimisation (PSO) are reviewed. Since PSO is a very common optimisation algorithm, this paper can(More)
Some real-world optimisation problems are dynamic; that is, their objective function and/or constraints vary over time. Solving such problems is very challenging. Particle swarm optimisation (PSO) is a well-known and efficient optimisation algorithm. In this paper, the PSO variants, devised for dynamic optimisation problems, are reviewed. This is the first(More)
Teaching–learning-based optimisation (TLBO) is an emerging gradient-free optimisation algorithm inspired by interactions between students and teacher in classrooms. TLBO has no control parameter to be tuned by user. This property makes it popular in research community. It has been successfully applied to challenging optimisation problems in different areas.(More)