Abstract
This paper presents a novel bioinspired algorithm to tackle complex optimization problems: the coral reefs optimization (CRO) algorithm. The CRO algorithm artificially simulates a coral reef, where different corals (namely, solutions to the optimization problem considered) grow and reproduce in coral colonies, fighting by choking out other corals for space in the reef. This fight for space, along with the specific characteristics of the corals' reproduction, produces a robust metaheuristic algorithm shown to be powerful for solving hard optimization problems. In this research the CRO algorithm is tested in several continuous and discrete benchmark problems, as well as in practical application scenarios (i.e., optimum mobile network deployment and off-shore wind farm design). The obtained results confirm the excellent performance of the proposed algorithm and open line of research for further application of the algorithm to real-world problems.
Highlights
In the last years, huge research efforts have been conducted towards solving hard optimization problems, by well balancing the trade-off between the complexity incurred by the utilized method and the optimality of the produced solutions
The experimental discrete-variable benchmark follows by applying the proposed coral reefs optimization (CRO) algorithm to the traveling salesman problem (TSP) [45], which is a classical combinatorial optimization problem defined in the following way: given a finite number of cities along with the distance between each pair of them, the TSP consists of finding the shortest possible route visiting each city exactly once and returning to the origin city
Coined as the mobile network deployment problem (MNDP), it essentially consists of positioning a set of base transceiver stations (BTS) controllers taking into account a threefold criterion: maximization of the coverage, minimization of the deployment cost, and minimization of the electromagnetic pollution generated by newly allocated base stations controllers
Summary
Huge research efforts have been conducted towards solving hard optimization problems, by well balancing the trade-off between the complexity incurred by the utilized method and the optimality of the produced solutions These problems, often characterized by search spaces of high dimensionality (either discrete or continuous), nonlinear objective functions and/or stringent constraints arise frequently in science and engineering applications. Ant colonies optimization (ACO) [5] is based on the social behavior of ants, whereas artificial immune system (AIS) algorithms [6] focus on imitating the behavior of the immune system in animals In this same line of research, particle swarm optimization (PSO) approaches [7] are in essence elegant algorithms specially well-suited for continuous optimization problems.
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