In the real world, it has been strongly desired to develop an algorithm for solving an optimization problem with many variables under strictly limiting the number of function calls. Because of this kind of reason, evolutionary computation competition 2017 in evolutionary computation symposium hosted by the JSEC was designed for enhancing the development of practical optimization algorithms. The main features of this competition are that the benchmark problem is "Benchmark Problem Based on Real-World Car Structure Design Optimization(Mazda Benchmark Problem)" created from the actual real problem in the car company and the computational condition for optimizing this problem is so strict. In this competition, the number of function calls is limited to only 30,000 even though the number of variables of this problem is over 200 and the landscape of this problem is multi-modal. This paper presents the winning algorithm of this competition in the single-objective category and tries to reveal the reasons why this algorithm could work so effectively in competition problem. This algorithm is based on estimating a high potential search area by iterating solution sampling like Estimation of Distribution Algorithm (EDA) and has a mechanism for improving the algorithm's efficiency. The most important points of this algorithm are very simple and with no unnecessary mechanisms. Through applying this algorithm to not only the competition benchmark problem but also some typical test problems, the effectiveness of this algorithm was confirmed and the characteristics of this algorithm were analyzed.
Recently, some discrete swarm intelligence algorithms such as particle swarm optimization with hamming distance (HDPSO), similarity artificial bee colony (S-ABC), and discrete firefly algorithm (DFA) have been proposed to solve graph 3-coloring problems (3-GCP) and obtain good results. However, these algorithms use static parameter settings that limit their performance on graphs with various sizes and topology. In this paper, we propose a discrete adaptive artificial bee colony (A-ABC) algorithm that can adjust the parameter automatically during the evolution according to the graph size and the fitness of candidates. For the convenience of comparison, we also propose a fixed ABC (F-ABC), which is identical to A-ABC but using fixed parameter setting during the evolution. A-ABC is simple and high performance. Experiments on 3-GCP show that A-ABC dramatically outperforms its competitors F-ABC, HDPSO, S-ABC, and DFA. We also study the scout bee phase and report that the scout bee phase is not required in solving 3-GCP