Augmented Downhill Simplex Method (ADSM) is introduced here, that is a heuristic combination of Downhill Simplex Method (DSM) with Random Search algorithm. In fact, DSM is an interpretable nonlinear local optimization method. However, it is a local exploitation algorithm, so, it can be trapped in a local minimum. In contrast, random search is a global exploration, but less efficient. Here, random search is considered as a global exploration operator in combination with DSM as a local exploitation method. Thus, presented algorithm is a derivative-free, fast, simple and nonlinear optimization method that is easy to be implemented numerically. Efficiency and reliability of the presented algorithm are compared with several other optimization methods, namely traditional downhill simplex, random search and steepest descent. Simulations verify the merits of the proposed method.

Investment decision making is one of the key issues in financial management. Selecting the appropriate tools and techniques that can make optimal portfolio is one of the main objectives of the investment world. This study tries to optimize the decision making in stock selection or the optimization of the portfolio by means of the artificial colony of honey bee algorithm. To determine the effectiveness of the algorithm, its sharp criteria was calculated and compared with the portfolio made up of genes and ant colony algorithms. The sample consisted of active firms listed on the Tehran Stock Exchange from 2005 to 2015. The sample selected by the systematic removal method. The findings show that artificial bee colony algorithm functions better than the genetic and ant colony algorithms in terms of portfolio formation.

A new hybrid framework is proposed for optimization of orderable search spaces. It is based on fuzzy-membership of design variable alternatives to the optimal solution. The method has dynamic behavior since the membership values are assigned as any solution candidates arises during the search and they are summed based on their overlaps in the alternatives scope. A trail matrix is also utilized to indirectly share values of the fuzzy memberships further ranked regarding closeness of an individual to the optimal solution both in the objective function and in the design space scope. The method takes benefit of different random, vector-sum and probability-based walks to move new solutions toward the global optimum. Utilizing a generalized cooling procedure, the related thresholds are tuned to choose between different walk types in searching the design space and also a search refinement strategy is developed. Two variants of such a framework is then proposed and compared with each other in addition to some well-known procedures including genetic algorithm and particle swarm optimization. Test results with some treated problems, reveals the superior performance of the proposed algorithm and its special feature in adaptive tuning the diversity index during the search.

This paper presents a novel population-based meta-heuristic algorithm inspired by the game of tug of war. Utilizing a sport metaphor the algorithm, denoted as Tug of War Optimization (TWO), considers each candidate solution as a team participating in a series of rope pulling competitions. The teams exert pulling forces on each other based on the quality of the solutions they represent. The competing teams move to their new positions according to Newtonian laws of mechanics. Unlike many other meta-heuristic methods, the algorithm is formulated in such a way that considers the qualities of both of the interacting solutions. TWO is applicable to global optimization of discontinuous, multimodal, non-smooth, and non-convex functions. Viability of the proposed method is examined using some benchmark mathematical functions and engineering design problems. The numerical results indicate the efficiency of the proposed algorithm compared to some other methods available in literature.

Meta-heuristics have already received considerable attention in various fields of engineering optimization problems. Each of them employes some key features best suited for a specific class of problems due to its type of search space and constraints. The present work develops aPseudo-random Directional Search, PDS, for adaptive combination of suchheuristic operators. It utilizes a short term memory via indirect information share between search agents and the directional search inspired by natural swarms. Treated numerical examples illustrate the PDS performance in continuous and discrete design spaces.

This paper proposes a novel population-based meta-heuristic optimization algorithm, called Perfectionism SearchAlgorithm (PSA), which is based on the psychological aspects of perfectionism. The PSA algorithm takes inspiration from one of the most popular model of perfectionism, which was proposed by Hewitt and Flett. During each iteration of the PSA algorithm, new solutions are generated by mimicking different types and aspects of perfectionistic behavior. In order to have a complete perspective on the performance of PSA, the proposed algorithm is tested with various nonlinear optimization problems, through selection of 35 benchmark functions from the literature. The generated solutions for these problems, were also compared with 11 well-known meta-heuristics which had been applied to many complex andpractical engineering optimization problems. The obtained results confirm the high performance of the proposedalgorithm in comparison to the other well-known algorithms.