Abstract
Constrained optimization plays an important role in many decision-making problems and various real-world applications. In the last two decades, various evolutionary algorithms (EAs) were developed and still are developing under the umbrella of evolutionary computation. In general, EAs are mainly categorized into nature-inspired and swarm-intelligence- (SI-) based paradigms. All these developed algorithms have some merits and also demerits. Particle swarm optimization (PSO), firefly algorithm, ant colony optimization (ACO), and bat algorithm (BA) have gained much popularity and they have successfully tackled various test suites of benchmark functions and real-world problems. These SI-based algorithms follow the social and interactive principles to perform their search process while approximating solution for the given problems. In this paper, a multiswarm-intelligence-based algorithm (MSIA) is developed to cope with bound constrained functions. The suggested algorithm integrates the SI-based algorithms to evolve population and handle exploration versus exploitation issues. Thirty bound constrained benchmark functions are used to evaluate the performance of the proposed algorithm. The test suite of benchmark function is recently designed for the special session of EAs competition in IEEE Congress on Evolutionary Computation (IEEE-CEC′13). The suggested algorithm has approximated promising solutions with good convergence and diversity maintenance for most of the used bound constrained single optimization problems.
Highlights
Constrained optimization has indispensable applications and currently it is impossible to name a single industry that is not using optimization process
We propose a multiswarm-intelligence-based algorithm by integrating ant colony optimization (ACO) and firefly algorithm (FA) to solve unconstrained optimization problems
In the last two decades, evolutionary algorithms based on swarm intelligence were developed to solve various test suites of benchmark functions and real-world problems
Summary
Constrained optimization has indispensable applications and currently it is impossible to name a single industry that is not using optimization process. Ese gradientbased methods can be employed as local search optimizers They are not much effective in solving complex optimization problems having had high multimodality, discontinuity, and noisy landscapes. Evolutionary computation researchers have shown great interests in employing multiple search operators such as one-point, two-point, and uniform crossover operators, simplex crossover, tournament, ranking, stochastic, uniform sampling selection operator, clearing, crowding, sharing-based niching techniques, adaptive penalty, epsilon, and superiority of feasible constraint handling approaches while solving the complicated test suites of benchmark functions and many important real-world problems have had high complexity, noisy environment, imprecision, uncertainty, and vagueness in their structures [9]. Many researches have shown the general applicability of the ensemble strategy in solving diverse problems by using different populated optimization algorithms [29, 52, 53]. Many researches have shown the general applicability of the ensemble strategy in solving diverse problems by using different populated optimization algorithms [29, 52, 53]. e performance of the suggested MSIA was evaluated over recently designed benchmark functions for the special
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