Self-adaptive fitness formulation for constrained optimization

By Raziyeh Farmani, Jonathan A. Wright. IEEE Transactions on Evolutionary Computation, 2003

345 citations recorded by the source corpus.

A self-adaptive fitness formulation is presented for solving constrained optimization problems. In this method, the dimensionality of the problem is reduced by representing the constraint violations by a single infeasibility measure. The infeasibility measure is used to form a two-stage penalty that is applied to the infeasible solutions. The performance of the method has been examined by its application to a set of eleven test cases from the specialized literature. The results have been compared with previously published results from the literature. It is shown that the method is able to find the optimum solutions. The proposed method requires no parameter tuning and can be used as a fitness evaluator with any evolutionary algorithm. The approach is also robust in its handling of both linear and nonlinear equality and inequality constraint functions. Furthermore, the method does not require an initial feasible solution.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/49ae3f854e7a39fcb1f209394a9f6b2892ffc8bb

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Citations in the corpus

Ensemble of Constraint Handling Techniques (2010, background): “(6) Therefore, in [9], [31], the selection of individuals in all the three phases is based on a value determined by the overall constraint violation and objective values.”
Constrained Optimization by the ε Constrained Differential Evolution with Gradient-Based Mutation and Feasible Elites (2006, methodology): “Several methods that dynamically control the penalty coefficient have been proposed [17]–[19].”
Bat algorithm: a novel approach for global engineering optimization (2012, neutral): “99994 Fesanghary et al. (2008) N/A 231,024.”
A generic framework for constrained optimization using genetic algorithms (2005, methodology): “But a downside to that approach as pointed out in [14] is that for G10 only 6 out of 30 runs produced feasible solutions.”
A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization (2006, methodology): “TABLE V COMPARISON OF OUR ALGORITHM (INDICATED BY CW) WITH RESPECT TO SAFF [9], SMES [16], RY [17], IS-PAES [26], FSA [40], AND IRY [18] ON 13 BENCHMARK FUNCTIONS.”
Constraint Handling in Multiobjective Evolutionary Optimization (2009, methodology): “In adaptive penalty function methods [16], [17], information gathered from the search process will be used to control the amount of penalty added to infeasible individuals.”
Constraint-handling in nature-inspired numerical optimization: Past, present and future (2011, background): “Farmani and Wright [154] proposed a two-parts adaptive penalty function in which no penalty factors need to be defined by the user.”
Differential evolution with dynamic stochastic selection for constrained optimization (2008, neutral): “Ray and Liew [22] adopted intra and intersociety interactions within a formal society and the civilization model, and Farmani and Wright [ 23 ] applied a two-stage penalty to infeasible solutions with self-adaptive fitness formulation.”
An empirical study about the usefulness of evolution strategies to solve constrained optimization problems (2008, methodology): “5.2.1.3 SAFF against V(m þ 1) 2 ES.”
Parameter Control in Evolutionary Algorithms: Trends and Challenges (2015, methodology): “Similar methods were proposed by Tessema and Yen [191], Montemurro, Vicenti and Vannucci [151], and Farmani and Wright [60].”